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Essays on Media Bias

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Bias in Media Reporting and Its Effect on Public Opinion

Biases in the news media, perceptions of media bias in political context, research of the views of nietzsche and baudrillard on fake news, let us write you an essay from scratch.

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Media Biases and Misrepresentation of Law Enforcement

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Analysis of Media Bias in Florida’s Governorship Race

The effect of the economic development and market competition on media bias in china, mainstream-media content: does the media have a liberal bias, misrepresentation of muslims in the british media, the role of media representation of knife crime in london in creating a moral panic among uk citizens, laws to combat online falsehoods in singapore’s media, biases around the issue of gun control in the media, the manipulation of the general public through american media during the 2016 presidential elections, the freedom of speech and of the press violation in north korean media, mail online story about baroness patricia scotland, an analysis of prophecies and confirmation bias, perception vs reality in media portrayal of crime.

Media bias is the bias of journalists and news producers within the mass media in the selection of many events and stories that are reported and how they are covered. The term "media bias" implies a pervasive or widespread bias contravening of the standards of journalism, rather than the perspective of an individual journalist or article.

Coverage bias when media choose to report only negative news about one party or ideology. Gatekeeping bias when stories are selected or deselected, sometimes on ideological grounds. Statement bias when media coverage is slanted towards or against particular actors or issues.

Advertising bias, concision bias, content bias, corporate bias, decision-making bias, distortion bias, mainstream bias, partisan bias, sensationalism, structural bias, false balance, undue weight, speculative content, false timeliness, ventriloquism.

1. Groseclose, T., & Milyo, J. (2005). A measure of media bias. The quarterly journal of economics, 120(4), 1191-1237. (https://academic.oup.com/qje/article-abstract/120/4/1191/1926642) 2. Mullainathan, S., & Shleifer, A. (2002). Media bias. (https://www.nber.org/papers/w9295) 3. Gentzkow, M., & Shapiro, J. M. (2006). Media bias and reputation. Journal of political Economy, 114(2), 280-316. (https://www.journals.uchicago.edu/doi/abs/10.1086/499414) 4. Baron, D. P. (2006). Persistent media bias. Journal of Public Economics, 90(1-2), 1-36. (https://www.sciencedirect.com/science/article/abs/pii/S0047272705000216) 5. D'Alessio, D., & Allen, M. (2000). Media bias in presidential elections: A meta‐analysis. Journal of communication, 50(4), 133-156. (https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1460-2466.2000.tb02866.x) 6. Groeling, T. (2013). Media bias by the numbers: Challenges and opportunities in the empirical study of partisan news. Annual Review of Political Science, 16, 129-151. (https://www.annualreviews.org/doi/abs/10.1146/annurev-polisci-040811-115123) 7. Hamborg, F., Donnay, K., & Gipp, B. (2019). Automated identification of media bias in news articles: an interdisciplinary literature review. International Journal on Digital Libraries, 20(4), 391-415. (https://link.springer.com/article/10.1007/s00799-018-0261-y) 8. Qin, B., Strömberg, D., & Wu, Y. (2018). Media bias in China. American Economic Review, 108(9), 2442-76. (https://www.aeaweb.org/articles?id=10.1257/aer.20170947) 9. Lee, T. T. (2005). The liberal media myth revisited: An examination of factors influencing perceptions of media bias. Journal of Broadcasting & Electronic Media, 49(1), 43-64. (https://www.tandfonline.com/doi/abs/10.1207/s15506878jobem4901_4) 10. Park, S., Kang, S., Chung, S., & Song, J. (2009, April). NewsCube: delivering multiple aspects of news to mitigate media bias. In Proceedings of the SIGCHI conference on human factors in computing systems (pp. 443-452). (https://dl.acm.org/doi/abs/10.1145/1518701.1518772)

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• Media Analysis
• Social Media
• Effects of Social Media
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INSTITUTE FOR POLICY RESEARCH

Fake news, big lies: how did we get here and where are we going.

IPR experts explain how mis- and disinformation affect our lives and offer ideas for how to counter it

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I think that misinformation / disinformation about candidates is bad, but misinformation / disinformation that destroys trust in our political process, institutions, and the integrity of democracy itself is a thousand times worse.”

Erik Nisbet Communication and policy scholar and IPR associate

“Are we going to be a nation that lives not by the light of the truth but in the shadow of lies?” President Joe Biden asked the country on the first anniversary of the January 6 insurrection at the U.S. Capitol.

But distinguishing truth from lies can be a difficult task when every day Americans read and hear false “facts”—misinformation—and deliberately misleading information created to cause harm—disinformation.

IPR faculty experts have generated a noteworthy body of research across different disciplines that explores what drives people to believe in untruths—and how the U.S. may be especially susceptible to disinformation. They also examine how misinformation and disinformation have affected the media, our politics, and even our health.

Why It’s Easy to Believe Misinformation and Disinformation

How Misinformation and Disinformation Flourish in U.S. Media

Declining Trust in News

What About Social Media?

Misinformation, Disinformation, and Polarization

‘Fake News’ in Presidential Elections

Informational Distrust in the COVID-19 Era

Resisting the ‘Shadow of Lies’

Although propaganda meant to persuade via argument, rumor, misunderstanding, and falsehood goes back to at least ancient Greece, today misinformation and disinformation are at the center of debate and research. Scholars have identified “information disorder syndrome,” the creating or sharing of false information out of error—misinformation—or to mislead or cause harm—disinformation or mal-information.

Why do people believe in misinformation and disinformation? Psychologist and IPR associate David Rapp , who studies how people learn through reading, finds that memory is key.

In experiments, he finds that when people read incorrect information, even about trivial subjects they already know, they often become confused and remember the inaccuracies. Subsequently, they answer questions using the incorrect statements.

“You can build memories for the things you’ve read that can then get resuscitated or recalled later in your decision making,” he said, especially when people are not carefully considering what they read.

Repeating false information over and over again—such as that the 2020 election was fraudulent— can lead to building memories for the information. And repeated information is often easy to retrieve, which can lead to problems, Rapp explained.

“If you can easily retrieve something, you tend to think it’s more true than if it’s something that’s hard to think of,” he said.

The more familiar people are with information they remember, including lies, the more likely they are to believe it is true, communication and policy scholar and IPR associate Erik Nisbet adds. In his and IPR associate research professor Olga Kamenchuk ’s research, they note people might believe misinformation or disinformation they recall even if they do not recall if the source is credible.

Moreover, people are more likely to believe the content they read or listen to that reflects the same emotions—anger, sadness, or anxiety—that they presently feel.

“Certain emotional states might make you more open to misinformation,” Nisbet said.

Breeding familiarity through repetition and seeing one’s emotional state mirrored in content are examples of a “mental shortcut,” according to Nisbet, and together they make people more likely to accept false information as true.

Examples of media bias charts that map newspapers, cable news, and other media sources on a political spectrum are easy to find. Can understanding bias in news sources help clarify why people fall prey to misinformation and disinformation?

Stephanie Edgerly , media scholar and IPR associate, suggests that a better place to start is with people’s individual biases, rather than those of news sources. In examining how people make sense of news sources, she points to the audience’s understanding of whether the source was news or entertainment—its genre—and its political orientation.

But how people perceive political orientation varies widely. Some see the media world as conservative vs. liberal with no middle ground. Others position news outlets in surprising places, such as the very conservative woman Edgerly interviewed who only centered Fox News between right- and leftwing media.

“We need to be really careful about how we talk about media,” Edgerly said. “This either/or way of making sense of media is too reductive—it’s simplistic.”

She is also concerned that accusations of biased reporting—or worse—can backfire and lead people to lose trust in all sources.

“We’re in a moment where we give a lot of attention to what the negative sources, low quality, disinformation-prone sources, are doing,” Edgerly noted. “I see this as creating a narrative where people think: ‘There’s a lot of bad sources out there, I don’t know how to find good sources, and, therefore, I’m just not going to trust any of it.’”

For decades, the U.S. media market was known as apolitical, objective, and neutral, Edgerly points out, but that is no longer seen as true.

If people do not trust news sources, and there’s no general acceptance of where to find unbiased information, then misinformation and disinformation will likely continue to flourish, she says. In such a news environment, even fact-checking breaks down as a tool to change beliefs.

Media, technology, and society researcher and IPR associate Pablo Boczkowski explains that trust in news institutions, as well as political and social institutions, is declining in the U.S. as the country becomes more fractured.

In his research, Boczkowski shows that people view news reporting today as biased and polarized, and they are especially distrustful of news circulated via social media. They are also more concerned about the effects untrustworthy sources could have on others than on themselves.

He points out that an increase in the supply of misinformation does not necessarily imply an increase in the take up of misinformation. 

“Most of the conversation—both academic and in news and policy circles—about issues of misinformation and disinformation focuses on the supply side: How much there is, and known distribution issues, how rapidly it propagates,” he said. He questions the implicit assumption that if there is more misinformation and disinformation, they must have proportionally more impact on the audience.

“I know that is not necessarily the case when I look at our research outside of the United States, at least,” he continued.

Social media such as Facebook and Twitter are often blamed as top disseminators of misleading and fabricated information. In public opinion surveys like this one on healthcare workers, respondents point directly at social media channels as spreaders of false information. While some IPR researchers hold social media channels responsible for misleading people, others note these outlets are easy targets of blame.

Boczkowski questions our “post-2016 fixation on the dystopic consequences of information technology,” pointing out that misinformation and disinformation are “as old as humanity itself.”

Nisbet offered, “I honestly believe that our focus on social media is a bit of a canard.”

“It’s easier to talk about regulating social media and dealing with social media as a problem than what I believe are the underlying political, economic, social, and cultural drivers of this ‘information disorder,’” he continued. “Social media might be a symptom or maybe amplifies like when you have a comorbidity—but it’s not the cause of our problems.”

IPR political scientist James Druckman , who studies the origins of partisanship and the role of persuasion in politics, sees “a mutually reinforcing relationship” between disinformation and polarization.

He describes those holding more polarized opinions as also being more susceptible to considering information as biased, and therefore, more susceptible to partisan bias.

“That information may reinforce their polarized tendencies,” he explained.

“Yet what is less appreciated but equally concerning is false polarization where people have misinformation about the other side and that misperception fuels their own polarization,” he added. “They believe the other side is much more different and threatening than they actually are, and that breeds polarization with social and political consequences.”

In Nature Human Behaviour , Druckman and his co-authors note partisan media and social media’s contribution to partisan animosity, but they highlight other social and political causes as well.

“I would be hesitant to place all the blame for political ills on misinformation,” Druckman cautions. “There are equally, if not more crucial, social and institutional factors at play–such as demographic shifts and political institutions that were set up in ways that did not anticipate some of these shifts.”

Did misinformation and disinformation play a role in the 2016 and 2020 U.S. presidential elections?

As Nisbet notes, we know a good deal about false and misleading information and why people believe it. What we do not fully understand, according to his research , is the impact it has on people’s attitudes and behavior.

It may seem that “fake news” and social media conspiracy theories grew in size and importance. However, as Druckman points out, since we could not measure misinformation very well in the past, we do not really know its full impact on opinion.

“It remains unclear just how much misinformation is out there—most systematic studies suggest less than many think—and if it has changed, given we could not measure it as easily before,” he said.

During the 2016 campaign, candidates were the focus of misinformation and disinformation, Nisbet explains, much of it on social media and mainly about Democratic candidate Hillary Clinton. Those attacks ended after the election.

The press and social media were a bit savvier during the 2020 campaign, Nisbet says, about allowing the spread of misinformation. But after the election, a “deluge of misinformation” followed when Facebook eased up on its precautions.

“It was not about Biden. It was about the election results and electoral processes and the integrity of the election,” Nisbet explained. “So the timing and the nature of the misinformation/disinformation was very different in 2016 versus 2020.”

He sees the possible long-term effects of the spread of false information about election integrity as a huge concern.

“I think that misinformation/disinformation about candidates is bad,” Nisbet said. “But misinformation/disinformation that destroys trust in our political process, institutions, and the integrity of democracy itself is a thousand times worse.”

As we enter the third year of the COVID-19 pandemic, some IPR experts have turned to tracking how misinformation and disinformation affect people’s health and survival.

Much of Druckman’s recent research has been based on the regular surveys collected and analyzed since April 2020 by the COVID States Project of the university consortium of Northwestern, Northeastern, Harvard, and Rutgers, which he co-leads. In July 2021, the project reported that people who relied on Facebook for news about COVID had substantially lower vaccination rates than the overall U.S. population, and they were more likely to believe falsehoods, such as that vaccines alter DNA or contain tracking microchips, were factual.

A November 2021 survey finds that nearly three-quarters (72%) of healthcare workers believe that misinformation has negatively influenced people’s decision to seek care for or get vaccinated against COVID-19.

Nisbet is also studying the effects of online COVID-19 information on health decisions in research supported by the National Science Foundation.

“One of the main effects of exposure, or at least endorsement, of COVID misinformation is reducing public trust in scientists and medical experts,” he said. “The more you believe false or misleading information about COVID the more you’re likely to be distrustful of public health or scientific experts.”

Communication studies researcher and IPR associate Ellen Wartella investigates how Twitter users prior to the pandemic promoted vaccine misinformation and connected it with a decrease in vaccination rates for diseases such as measles and tetanus and growing distrust of science and public health.

She sees a similar pattern of misinformation about the COVID vaccine.

“It’s absolutely the case that social media has been the main conveyor and mechanism by which anti-vaxxers can spread their message,” she said.

Rapp contributed to the “COVID-19 Vaccine Communication Handbook & Wiki,” an international collaboration created to improve vaccine communication and fight misinformation. To combat misinformation about the COVID vaccine, he suggests trying to find common ground with people to begin to persuade them.

“It’s going to take a concerted effort among many constituents,” he said.

Perhaps the biggest question overhanging the research is, how can we combat misinformation?

Druckman notes that a host of techniques have been developed, such as literacy courses.

Fact-checking is a very limited tool, as Edgerly and Nisbet observe, because it depends on the audience trusting the source of the checking.

Nisbet suggests what he calls “prebunking,” an “inoculation” against misinformation ahead of its distribution.  For example, news organizations could have done more to publicize prior to the 2020 election that vote tallies would change overnight as mail-in ballots were added to the totals.

Rapp and Edgerly recommend scientists and journalists be more transparent about what they do.

“The general public largely doesn’t understand what journalists do, but they can recognize the power and importance of good journalism,” Edgerly said. She would like to see “a little bit of reminding the public about what journalism is supposed to do so it’s not tied into narratives about fake news and partisan bickering.”

Rapp encourages more “lateral reading” of different sources on the same subject—a technique endorsed in many classroom settings. He also suggests that academics, doctors, and politicians quit only speaking in jargon and in a top-down way about issues if we want to bridge the partisan divides exacerbated by misinformation and disinformation.

For Boczkowski, trust in institutions, including the media, is the fundamental issue. To restore trust, he says we must improve our institutions to work fairly for all groups, not just the privileged ones.

“Instead of spending so much time on [disinformation], we should spend all the energy we spend on that looking at what can we do to make our society more equitable, more just, more inclusive, to emphasize those that have been disenfranchised,” he said. “Otherwise, it’s like having a strep infection and thinking you’re going to cure it with Tylenol!”

Pablo Boczkowski is Hamad Bin Khalifa Al-Thani Professor in Communication Studies. James Druckman is Payson S. Wild Professor of Political Science and IPR associate director. Stephanie Edgerly is associate professor and director of research in the Medill School. Erik Nisbet is the Owen L. Coon Endowed Professor of Policy Analysis & Communication. David Rapp is professor of psychology. Ellen Wartella is Sheikh Hamad bin Khalifa Al-Thani Professor of Communication. All are IPR faculty members.

Photo credit: iStock

Published: January 26, 2022.

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Articles & Media

Books & eBooks

• Introduction
• Which Way Does Your News Lean?

How to Spot Media Bias

Featured articles & reports, featured books & ebooks, common democrat and republican phrases, featured open access.

• Using Biased Sources
• Where to Get Your News

When trying to spot media bias, ask yourself these questions: 

1. What kind of information is it? News? Opinion? Ad? News media can range from fairly unbiased to very biased. Opinion media are biased by definition. Media that are ads will be slanted to encourage you to buy a product. Ask yourself if the media appeals to your emotions or if it makes you think? If it appeals to your emotions, especially anger or outrage, it is more likely to be biased.  2. Who and what are the sources given and why should you believe them? Are the sources given? Are the sources scholars or experts on the topic? Are the sources associated with a political party or special interest group? If the sources are given with information on their expertise and associations you are better able to judge possible bias. If no sources are given it makes it very hard for you to judge, which is common with biased media.  3. What’s the evidence and how was it verified? What’s the evidence and how was it verified? Is the source a document? Witness? Or is it hearsay/speculation? The more vague that the media is, the more likely it is to be biased.  4. Is the main point proven by the evidence? Did the sources provided justify the conclusion or main point of the story? Based media will sometimes present sources and evidence that might seem related but don't actually prove the main point. This technique is known as smoke and mirrors. 

5. Were you encouraged to draw your own conclusion?  Unbiased media will present evidence and let you draw your own conclusion. Biased media will tell you what to think. 

6. What’s missing? Was there an aspect or point that was not covered or unclear that you are left wondering about? Was there any evidence presented that supported a possible alternate conclusion or was that left out? Biased media will typically only provide evidence supporting the conclusion that they want you to draw.

Based on American Press Institute .

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A study (PDF) on media bias from University of Chicago economists Matthew Gentzkow and Jesse Shapiro, used text as data to look at common Democratic and Republic phrases in Congress. You can consider these phrases when you are trying to determine if a source or politician is conservative of liberal. See some examples of terms frequently used by democrat's or republicans, below. You can read the study to find even more terms. 

• AllSides: 16 Types of Media Bias and How to Spot Them
• AllSides: Media Bias
• How Biased Is Your Media? A New Freakonomics Radio Podcast - Freakonomics Freakonomics

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Persuasive Essay Writing

Persuasive Essay About Social Media

Learn How to Write a Persuasive Essay About Social Media With Examples

Published on: Jan 26, 2023

Last updated on: Jan 29, 2024

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Are you looking to learn how to write a persuasive essay about social media? 

Perfect, you've come to the right place!

From navigating the power of hashtags to analyzing changes in public opinion, these examples will help guide you on your journey. 

Whether you’re a seasoned pro at writing persuasive essays or just a starter, look at these examples to be inspired.

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Brief Overview of Persuasive Essay

A persuasive essay persuades the reader or audience to take a particular stance on an issue. It is used to present an opinion on any subject, and it typically takes the form of an academic essay. It includes evidence and facts supporting its arguments.

The writer must use facts and reliable sources to back up his or her claims.

It is also important that the essay should be well-structured. It should have clear arguments and a logical flow from one point to another.

Learn more about crafting perfect persuasive essays with the help of our detailed guide.

Persuasive Essay Examples About Social Media

Are you a student unsure how to write persuasive essays successfully? Well, never fear! 

We've got examples of some amazing persuasive essays about social media that will surely give you inspiration. Let’s take a look at a short persuasive essay example: 

Check these FREE downloadable samples of persuasive essays! 

Persuasive essay about social media on students

Persuasive essay about social media addiction

Persuasive Essay about Social Media Platforms are Danger to Our Privacy

Persuasive essay about social media beneficial or harmful

Persuasive essay about social media privacy

Persuasive essay on social media is bad for students

Examples of Argumentative Essay about Social Media

To help get your creative juices flowing, look at these example argumentative essays about social media below!

Argumentative essay about social media advantages and disadvantages

Argumentative essay about social media addiction

For more examples of persuasive essays, check out our blog on persuasive essay examples .

How Can You Write a Persuasive Essay About Social Media?      

A persuasive essay about social media can be an interesting and challenging task.

Understanding what makes a persuasive essay unique and how to craft arguments that effectively communicate your point of view is important. 

These are a few steps you should follow before writing an effective persuasive essay on social media.

Step 1: Decide Your Stance

First, you must decide on your stance regarding the issue at hand. Are you for or against the use of social media? Are you in support of social media?

After you decide your stance, move on to the research process.

Step 2: Conduct Due Research

Once you have established your position, you must research the topic and develop an argument that supports your stance. 

Make sure to include facts, statistics, and examples to back up your points.

Step 3: Outline Your Essay

Create a structured persuasive essay outline before delving into detailed writing. This roadmap will help organize your thoughts, ensuring a logical flow of arguments. Outline your introduction, key points, counterarguments, and conclusion.

Step 4: Craft Your Introduction 

The introduction should provide context, state the thesis statement , and grab the reader's attention. It precedes deciding your stance and initiates the overall writing process.

Read this free PDF to learn more about crafting essays on social media!

Persuasive essay about social media introduction

Step 5: Write the Body

Organize your arguments logically in the body of the essay. Each paragraph should focus on a specific point, supported by research and addressing counterarguments. This follows the introduction and precedes maintaining a persuasive tone.

Step 6: Address All Counterarguments

It is important to anticipate potential counterarguments from those who oppose your stance. 

Take time to address these points directly and provide evidence for why your opinion is more valid.

Step 7: Maintain a Persuasive Tone

To maintain your audience's attention, it is important to write in a confident and persuasive tone throughout the essay. 

Use strong language that will make readers take notice of your words. 

Check out this video on persuasive writing tones and styles.

Step 8: Conclude Your Essay

Finally, end your essay with a memorable conclusion that will leave your audience with something to think about. 

With these important steps taken into account, you can create an effective persuasive essay about social media!

Step 9: Revise and Edit

After completing your initial draft, take time to revise and edit your essay. Ensure clarity, coherence, and the effective flow of arguments. This step follows the conclusion of your essay and precedes the final check for overall effectiveness.

Persuasive Essay About Social Media Writing Tips

Here are some additional writing tips to refine your persuasive essay on social media.

• Highlight Numbers: Use facts and numbers to show how important social media is.
• Tell Stories: Share real stories to help people connect with the impact of social media.
• Use Pictures: Add charts or pictures to make your essay more interesting and easy to understand.
• Answer Questions: Think about what people might disagree with and explain why your ideas are better.
• Talk About What's Right: Explain why it's important to use social media in a good and fair way.

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Social Media Persuasive Essay Topics

Take a look at these creative and enticing persuasive essay topics. Choose from one of them or get inspiration from these topics.

• Should social media platforms be held accountable for cyberbullying?
• Should age restrictions be stricter for social media access to protect younger users from its negative effects?
• Should social media companies be mandated to prioritize user privacy over targeted advertising?
• Should schools integrate mandatory education on the pitfalls of social media for students?
• Should governments regulate the amount of time users spend on social media to prevent addiction?
• Should social media influencers face stricter guidelines for promoting unrealistic body standards?
• Should there be more transparency about how algorithms on social media platforms amplify divisive content?
• Should employers be allowed to consider an applicant's social media profiles during the hiring process?
• Should there be penalties for social networking sites that propagate false information?
• Should there be a limit on the amount of personal data social media platforms can collect from users?

Check out some more interesting persuasive essay topics to get inspiration for your next essay.

Wrapping up, 

Learning how to write persuasive essays about social media matters in today's digital world is crucial whether you are a high school student or a college student. These examples guide us in exploring both the good and bad sides of social media's impact. 

We hope this persuasive blog on social media has given you a few new ideas to consider when persuading your audience.

But if you are struggling with your essay assignment do not hesitate to seek professional help. At CollegeEssay.org , our writing experts can help you get started on any type of essay. 

With our professional persuasive essay writing service , you can be confident that your paper will be written in utmost detail.

So don't wait any longer! Just ask us ' write my essay ' today and let us help you make the most of your writing experience!

Frequently Asked Questions

What are some good persuasive essay topics.

Good persuasive essay topics can include topics related to social media, such as 

• whether or not it should be regulated more heavily,
• the impact of social media on society, 
• how social media has changed our daily lives.

How do you write an introduction for social media essay?

You should start by briefly explaining what the essay will cover and why it is important. 

You should also provide brief background information about the topic and what caused you to choose it for your essay.

What is a good title for a social media essay?

A good title for a social media essay could be "The Impact of Social Media on Society" or "Social Media: Regulation and Responsibility." 

These titles indicate the content that will be discussed in the essay while still being interesting and thought-provoking.

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Chapter 6: Bias in Writing and Research

Reading and writing critically, style and tone of language.

Just as with any essay, the way you write and the tone that you use is very important to consider.  If you are talking with a person who uses aggressive and inflammatory words, are you more or less likely to listen to the whole argument and ultimately be convinced? If someone is waving his hands and swearing or yelling, the gestures and raised voice may actually distract you from what is being said.  Also, when people are extremely animated in their discussions, their audience may become defensive if they do not agree with the ideas presented. In such a case, the audience may then respond in the same way, and no one ends up really hearing other points of view and will definitely not be convinced.

In short, remember to choose your words carefully. While you will need to use assertive language to support your ideas, you need to choose objective words. How you make your argument more convincing is by: Using strong, peer-reviewed, and reliable evidence to back up your ideas; presenting and rebutting at least one opposing idea

Acknowledging Opposing Ideas and Limits to Your Argument

Because an argument implies differing points of view on the subject, you must be sure to acknowledge those opposing ideas. Avoiding ideas that conflict with your own gives the reader the impression that you may be uncertain, fearful, or unaware of opposing ideas. Thus, it is essential that you not only address counterarguments but also do so respectfully.

Try to address opposing arguments earlier rather than later in your essay. Rhetorically speaking, ordering your positive arguments last allows you to better address ideas that conflict with your own, so you can spend the rest of the essay countering those arguments. This way, you leave your reader thinking about your argument rather than someone else’s. You have the last word.

Acknowledging different points of view also fosters more credibility between you and the audience. They know from the outset that you are aware of opposing ideas and that you are not afraid to give them space.

It is also helpful to establish the limits of your argument and what you are trying to accomplish. In effect, you are conceding early on that your argument is not the ultimate authority on a given topic. Such humility can go a long way toward earning credibility and trust with an audience. Your readers will know from the beginning that you are a reasonable writer, and they will trust your argument as a result. For example, in the following concessionary statement, the writer advocates for stricter gun control laws, but admits it will not solve all of our problems with crime:

Although tougher gun control laws are a powerful first step in decreasing violence in our streets, such legislation alone cannot end these problems since guns are not the only problem we face.

Such a concession will be welcome by those who might disagree with this writer’s argument in the first place. To effectively persuade their readers, writers need to be modest in their goals and humble in their approach to get readers to listen to the ideas.

Phrases of Concession  are those such as:

• granted that

Bias in Writing

Everyone has various biases on any number of topics. For example, you might have a bias toward wearing black instead of brightly coloured clothes, or wearing jeans rather than formal wear. You might have a bias toward working at night rather than in the morning, or working by deadlines rather than getting tasks done in advance. These examples identify minor biases, of course, but they still indicate preferences and opinions.

Bias and angles can easily appear even through the smallest words you choose to use in your writing. Choosing each word carefully is even more significant in a persuasive paper because, as already mentioned, you want your reader to view your presentation of ideas as logical and not just a tirade. Using objective and neutral language and evidence and acknowledging you have a possible bias will help you present a well-rounded and developed argument.

Handling bias in writing and in daily life can be a useful skill. It will allow you to articulate your own points of view while also defending yourself against unreasonable points of view. The ideal in persuasive writing is to let your reader know your bias, but do not let that bias blind you to the primary components of good argumentation: sound, thoughtful evidence and a respectful and reasonable address of opposing sides.

• The strength of a personal bias is that it can motivate you to construct a strong argument. If you are invested in the topic, you are more likely to care about the piece of writing. Similarly, the more you care, the more time and effort you are apt to put forth and the better the final product will be.
• The weakness of personal bias is that it can take over the essay—when, for example, you neglect opposing ideas, exaggerate your points, or repeatedly insert yourself ahead of the subject by using I too often. Being aware of all three of these pitfalls will help you avoid them.

Video source: https://youtu.be/ccK9TTA6xiw

Fact versus Opinion

Facts are statements that can be definitely proven using objective data. The statement that is a fact is absolutely valid. In other words, the statement can be pronounced as true or false. For example, 2 + 2 = 4. This expression identifies a true statement, or a fact, because it can be proved objectively .

Opinions are subjective and include personal views, or judgments. An opinion is what an individual believes about a particular subject. However, an opinion in argumentation must have legitimate backing; adequate evidence and credibility should support the opinion. Consider the credibility of expert opinions, as experts in a given field have the knowledge and credentials to make their opinion meaningful to a larger audience.

For example, you seek the opinion of your dentist when it comes to the health of your gums, and you seek the opinion of your mechanic when it comes to the maintenance of your car. Both have knowledge and credentials in those respective fields, which is why their opinions matter to you. But the authority of your dentist may be greatly diminished should he or she offer an opinion about your car, and vice versa.

In your writing, you want to strike a balance between credible facts and authoritative opinions. Relying on one or the other will likely lose more of your audience than it gains.

The Use of THE FIRST PERSON (“i”) in Writing

The use of I in writing is often a topic of debate, and the acceptance of its usage varies from instructor to instructor. It is difficult to predict the preferences for all your present and future instructors, but consider the effects it can potentially have on your writing.

Be mindful of the use of I in your writing because it can make your argument sound overly biased, for two primary reasons:

Excessive repetition of any word will eventually catch the reader’s attention—and usually not in a good way. The use of I is no different.

The insertion of I into a sentence alters not only the way a sentence might sound but also the composition of the sentence itself. I is often the subject of a sentence. If the subject of the essay is supposed to be, say, smoking, then by inserting yourself into the sentence, you are effectively displacing the subject of the essay into a secondary position. In the following example, the subject of the sentence is bolded and underlined:

Smoking is bad. vs. I think smoking is bad.

In the first sentence, the rightful subject, smoking , is in the subject position in the sentence. In the second sentence, the insertion of I and think replaces smoking as the subject, which draws attention to I and away from the topic that is supposed to be discussed. Remember to keep the message (the subject) and the messenger (the writer) separate.

You can use the following checklist for good arguments as you work on your persuasive essay:

Developing Sound Arguments

• Does my essay contain the following elements?
• An engaging introduction
• A reasonable, specific thesis that is able to be supported by evidence
• A varied range of evidence from credible sources
• Respectful acknowledgment and explanation of opposing ideas
• A style and tone of language that is appropriate for the subject and audience
• Acknowledgment of the argument’s limits
• A conclusion that will adequately summarize the essay and reinforce the thesis

Using Visual Elements to Strengthen Arguments

Adding visual elements to a persuasive argument can often strengthen its persuasive effect. However, remember you want to use them to make a bigger impact for your reader, so you need to make sure they are:

• Relevant and essential. They should help your reader visualize your point.
• Easy to follow. The reader should not have to work too hard to understand.
• Appropriate to audience, tone, and purpose. Always keep the audience in mind.
• Appropriately cited and referenced. If you borrow from a source, be sure to include proper citations.
• NOT disrespectful. You want your writing to been seen as fair and non-biased.
• NOT used too often. They will become more of a distraction than a focal point if they are used too often

There are two main types of visual elements: quantitative visuals and qualitative visuals.

• Quantitative visuals present data graphically. They allow the audience to see statistics spatially. The purpose of using quantitative visuals is to make logical appeals to the audience. For example, sometimes it is easier to understand the disparity in certain statistics if it is displayed graphically. Bar graphs, pie charts, Venn diagrams, histograms, and line graphs are all ways of presenting quantitative data in spatial dimensions.
• Qualitative visuals present images that appeal to the audience’s emotions. Photographs and pictorial images are examples of qualitative visuals. Such images often try to convey a story, and seeing an actual example can carry more power than hearing or reading about the example. For example, one image of a child suffering from malnutrition will likely have more of an emotional impact than pages dedicated to describing that same condition in writing.

Writing at Work

When making a business presentation, you typically have limited time to get your idea across. Providing visual elements for your audience can be an effective timesaving tool. Quantitative visuals in business presentations serve the same purpose as they do in persuasive writing. They should make logical appeals by showing numerical data in a spatial design. Quantitative visuals should be pictures that might appeal to your audience’s emotions. You will find that many of the rhetorical devices used in writing are the same ones used in the workplace.

Writing for Academic and Professional Contexts: An Introduction Copyright © 2023 by Sheridan College is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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How do we raise media bias awareness effectively? Effects of visualizations to communicate bias

Timo spinde.

1 Department of Computer and Information Science, University of Konstanz, Konstanz, Germany

2 School of Electrical, Information and Media Engineering, University of Wuppertal, Wuppertal, Germany

Christin Jeggle

3 Department of Psychology, University of Konstanz, Konstanz, Germany

Magdalena Haupt

Wolfgang gaissmaier, helge giese, associated data.

Data are available at https://osf.io/e95dh/ .

Media bias has a substantial impact on individual and collective perception of news. Effective communication that may counteract its potential negative effects still needs to be developed. In this article, we analyze how to facilitate the detection of media bias with visual and textual aids in the form of (a) a forewarning message, (b) text annotations, and (c) political classifiers. In an online experiment, we randomized 985 participants to receive a biased liberal or conservative news article in any combination of the three aids. Meanwhile, their subjective perception of media bias in this article, attitude change, and political ideology were assessed. Both the forewarning message and the annotations increased media bias awareness, whereas the political classification showed no effect. Incongruence between an articles’ political position and individual political orientation also increased media bias awareness. Visual aids did not mitigate this effect. Likewise, attitudes remained unaltered.

Introduction

The Internet age has a significant impact on today’s news communication: It allows individuals to access news and information from an ever-increasing variety of sources, at any time, on any subject. Regardless of journalistic standards, media outlets with a wide reach have the power to affect public opinion and shape collective decision-making processes [ 1 ]. However, it is well known that the wording and selection of news in media coverage often are biased and provide limited viewpoints [ 2 ], commonly referred to as media bias . According to Domke and colleagues [ 3 ], media bias is a structural, often wilful defect in news coverage that potentially influences public opinion. Labeling named entities with terms that are ambiguous in the concepts they allude to (e.g. "illegal immigrants" and "illegal aliens" [ 4 ] or combining concepts beyond their initial contexts into figurative speech that carry a positive or negative association ("a wave of immigrants flooded the country") can induce bias. Still, the conceptualization of media bias is complex since biased and balanced reporting cannot be distinguished incisively [ 5 ]. Many definitions exist, and media bias, in general, has been researched from various angles, such as psychology [ 6 ], computer science [ 7 ], linguistics [ 8 ], economics [ 9 ], or political science [ 10 ]. Therefore, we believe advancement in media bias communication is relevant for multiple scientific areas.

Previous research shows the effects of media bias on individual and public perception of news events [ 6 ]. Since the media are citizens’ primary source of political information [ 11 ], associated bias may affect the political beliefs of the audience, party preferences [ 12 ] and even alter voting behavior [ 13 ]. Moreover, exposure to biased information can lead to negative societal outcomes, including group polarization, intolerance of dissent, and political segregation [ 14 ]. It can also affect collective decision-making [ 15 ]. The implications of selective exposure theory intensify the severity of biased news coverage: Researchers observed long ago that people prefer to consume information that fits their worldview and avoid information that challenges these beliefs [ 16 ]. By selecting only confirmatory information, one’s own opinion is reaffirmed, and there is no need to re-evaluate existing stances [ 17 ]. In this way, the unpleasant feeling of cognitive dissonance is avoided [ 18 ]. Isolation in one’s own filter bubble or echo chamber confirms internal biases and might lead to a general decrease in the diversity of news consumption [ 14 ]. This decrease is further exacerbated by recent technological developments like personalized overview features of, e.g., news aggregators [ 19 ]. How partisans select and perceive political news is thus an important question in political communication research [ 20 ]. Therefore, this study tries to test ways to increase the awareness of media bias (which might mitigate its negative impact) and the partisan evaluation of the media through transparent bias communication.

Media bias communication

Media bias occurs in various forms, for example, whether or how a topic is reported (D’Alessio & Allen, 2000) and may not always be easy to identify. As a result, news consumers often engage with distorted media but are not aware of it and exhibit a lack of media bias awareness [ 21 ]. To address this issue, revealing the existence and nature of media can be an essential route to attain media bias awareness and promote informed and reflective news consumption [ 19 ]. For instance, visualizations may generally help to raise media bias awareness and lead to a more balanced news intake by warning people of potential biases [ 22 ], highlighting individual instances of bias [ 19 ], or facilitating the comparison of contents [ 2 , 23 ].

Although knowledge of how to communicate media bias effectively is crucial, visualizations and enhanced perception of media bias have only played a minor role in existing research, and several approaches have not yet been investigated. Therefore, this paper tests how effectively different strategies promote media bias awareness and thereby may also help understand common barriers to informed media consumption. We selected three major methods in related work [ 19 , 22 ] on the topic to further investigate them in one combined study: forewarning messages, text annotations, and political classifications. Theoretical foundations of bias messages and visualizations are yet scarce, and neither in visualization theory nor in bias theory, suitable strategies in the domain have been extensively tested.

Forewarning message

According to socio-psychological inoculation theory [ 24 ], it is possible to pre-emptively confer psychological resistance against persuasion attempts by exposing people to a message of warning character. It is similar to the process of immunizing against a virus by administering a weakened dose of the virus: A so-called inoculation message is expected to protect people from a persuasive attack by exposing them to weakened forms of the persuasion attempt. Due to the perceived threat of the forewarning inoculation message, people tend to strengthen their own position and are thus more resistant to influences of imminent persuasion attacks [ 25 ]. Therefore, one strategy to help people detect bias is to prepare them ahead of media consumption that media bias may occur, thereby "forewarning" them against biased language influences. Such warnings have been widely established in persuasion and shown to be effective in different applied contexts [ 26 ]. Furthermore, such warnings also seem to help not only to protect attitudes against influences but also to determine the quality of a piece of information [ 27 – 29 ] and communicate the information accordingly [ 30 ]. For biased language, this may work specifically by focusing the reader’s attention on a universal motive to evaluate the accuracy of information while relying on the individual’s capacity to detect the bias when encountered [ 30 ]; Bolsen & Druckman, 2015).

Annotations

Other than informing people in advance about bias occurrence, a further approach is to inform them during reading, thereby increasing their awareness of biased language and providing direct help to detect it in an article. Recently, there has been a lot of research on media bias from information science, but it is mainly concerned with its identification and detection [ 31 – 34 ]. However, whereas some research concerning the effects of visualizations of media bias in news articles to detect bias are promising (here: flagging fake news as debunked [ 35 ]) others did not find such effects, potentially also due to the technical issues in accurately annotating single articles [ 19 ]. Still, they offer a good prospect to enable higher media bias awareness and more balanced news consumption. We show our annotation visualization in Fig 1 .

Example of the bias annotation "subjective term". Boxed annotation appeared by moving the cursor/finger over the highlighted text section.

Political classification

Another attempt to raise media bias awareness is a political classification of biased material after readers have dealt with it. An and colleagues [ 36 ] proposed an ideological left-right map where media sources are politically classified. The authors suggest that showing a source’s political leaning helps readers question their attitudes and even promotes browsing for news articles with multiple viewpoints. Likewise, several other studies indicate that feedback on the political orientation of an article or a source may lead to more media bias awareness and a more balanced news consumption [ 19 ]. Additionally, exposing users to multiple diverse viewpoints on controversial topics encourages the development of more balanced viewpoints [ 23 ]. A study of Munson and colleagues (2013) further suggests that a feedback element indicating whether the user’s browsing history consists of biased news consumption modestly leads to a more balanced news consumption. Based on these findings, we will test whether the sole representation of a source’s leaning helps raise bias awareness among users on the condition that the article is classified as politically skewed. We show our political classification bar in Fig 2 .

Example of an article classification as being politically left-oriented.

Partisan media bias awareness

Attempts to raise media bias awareness may be further complicated by the fact that the detection of media bias and the evaluation of news seem dependent on the political ideology of the beholder [ 37 – 41 ]. However, this partisan effect is not only apparent in neutral reporting: It is supposed that individuals perceive biased content that corresponds to their opinion as less biased [ 38 ] and biased content that contradicts their viewpoints as more biased [ 41 ].

These findings suggest that incongruence between the reader’s position and the news article’s position may increase media bias perception of the article, whereas congruence may decrease it. Thus, partisan media consumers may engage in motivated reasoning to overcome cognitive dissonance experienced when encountering media bias in any news article generally in line with their viewpoints [ 42 ]. According to Festinger [ 18 ], cognitive dissonance is generated when a person has two cognitive elements that are inconsistent with each other. This inconsistency is assumed to produce a feeling of mental discomfort. People who experience dissonance are motivated to reduce the inconsistency because they want to avoid or reduce this negative emotion.

Furthermore, Festinger notes that exposure to messages inconsistent with one’s beliefs could create cognitive dissonance, leading people to avoid or reduce negative emotions. In line with this notion, raising media bias awareness could increase experienced cognitive dissonance and thereby lead to even more partisan ratings of bias. Another explanation of the phenomenon of partisan bias ratings is varying norms about what content is considered appropriate in media coverage dependent on one’s political identity[ 43 ]. Other researchers focus on the inattention to the quality of news and the motive to only support truthful news [ 44 ]. Both approaches lead us to expect opposite results for the partisanship of the media bias ratings with increased media bias awareness as created by our proposed visualizations: Partisanship of ratings should decrease rather than increase as people are reminded of more general norms and accuracy motives [ 27 ].

Study aims and hypotheses

This project aims to contribute to a deeper understanding of effective media bias communication. To this end, we create a set of bias visualizations revealing bias in different ways and test their effectiveness to raise awareness in an online experiment. Following the respective literature elaborated above for each technique, we would expect enhanced media bias awareness by all visualizations:

• H1a: A forewarning message prior to news articles increases media bias awareness in presented articles.
• H1b: Annotations in news articles increase media bias awareness in presented articles.
• H1c: A political classification of news articles increases media bias awareness in presented articles.

Another goal of this study is to understand better the reader’s political orientation in media bias awareness. In line with the findings of partisan media bias perception (hostile media effect; Vallone et al., 1985), we adopt the following hypothesis:

• H2: Presented material will be rated less biased if consistent with individual political orientation.

Furthermore, we assume, following the attentional and normative explanation of partisanship in ratings rather than cognitive dissonance theory, the following effect:

• H3: Bias visualizations will mitigate the effects of partisan bias ratings.

Participants

A total of 1002 participants from the US were recruited online via Prolific in August of 2020. A final sample of N = 985 was included in the analysis (51% female; age : M = 32.67; SD = 11.95 ) . The excluded participants did not fully complete the study or indicated that their data might not be trusted in a seriousness check. The target sample size was determined using power analysis, so that small effects ( f = 0.10) could be found with a power of .80 [ 45 ]. The online study was scheduled to last approximately 10 minutes, for which the participants received £1.10 as payment.

Design and procedure

The experiment was conducted online in Qualtrics ( https://www.qualtrics.com ). It operated with fully informed consent, adheres to the Declaration of Helsinki, and was conducted in compliance with relevant laws and institutional guidelines, including the ones of the University of Konstanz ethics board. All participants confirmed their consent in written form and were informed in detail about the study, the aim, data processing, anonymization, and other background information.

After collecting informed consent and demographic information, we conducted an initial attitude assessment which asked for their general perception of the presented topic on three dimensions and personal relevance. Next, participants read one randomly selected biased news article (either liberal or conservative), randomly supplemented by any combination of the visual aids (forewarning message, annotations, political classification). Thus, the study had a 2x2x2 forewarning message (yes/no) x annotations (yes/no) x political map (yes/no) between design. The article also varied between participants in both article position (liberal/conservative) and article topic (gun law/abortion) to determine the results’ partialness and generalizability. Finally, attitudes towards the topic were reassessed, followed by a seriousness check.

Study material

Visual aids.

Forewarning message . The forewarning message consisted of a short warning and was displayed directly before the news article. It reads: " Beware of biased news coverage . Read consciously . Don’t be fooled . The term ’media bias’ refers to , in part , non-neutral tonality and word choice in the news . Media Bias can consciously and unconsciously result in a narrow and one-sided point of view . How a topic or issue is covered in the news can decisively impact public debates and affect our collective decision making ." Besides, an example of one-sided language was shown, and readers were encouraged to consume news consciously.

Annotations . Annotations were directly integrated into the news texts. Biased words or sentences were highlighted [ 46 ], and by hovering over the marked sections, a short explanation of the respective type of bias appeared. For example, if moving the cursor over a very one-sided term, the following annotation would be displayed: " Subjective term : Language that is skewed by feeling , opinion or taste ." Annotations were based on ratings of six members of our research group, where phrases had to be nominated by at least three raters. The final annotations can be found in the supplementary preregistration repository accompanying this article at https://osf.io/e95dh/‌?view_only=d2fb5dc‌2d64741e393b30b9ee6cc7dc1 (Non-anonymous Link is made accessible in case of acceptance). We followed the guidelines applied in existing research to teach annotators about bias and reach higher-quality annotations [ 47 ]. In future work, we will further increase the number of raters, as we address in the discussion.

Political classification . A political classification in the form of a spectrum from left to right indicated the source’s political ideology. It was displayed immediately after the presented article and based on the rating of the webpage Allsides.

We used four biased news articles that varied in topic and political position. Each participant was assigned to one article. The two topics covered were gun law and the debate on abortion, with either a liberal or conservative article position. Topics were selected because we considered them controversial issues in the United States that most people are presumably familiar with. To ensure that articles were biased, they were taken from sources deemed extreme according to the Allsides classification. Conservative texts were taken from Breitbart.com ; liberal articles were from Huffpost.com and Washingtonpost.com . We also conducted a manipulation check to determine whether participants perceived political article positions in line with our assumptions: Just after reading the article, participants were asked to classify its political stance on a visual analogue scale (-5 = very liberal to 5 = very conservative ). To ensure comparability, articles were shortened to approximately the same length, and respective sources were not indicated. All article texts used are listed together with their annotations in the supplementary preregistration repository accompanying this article (we show the link on the previous page).

Media bias awareness

Five semantic differentials assessed media bias awareness on fairness, partialness, acceptableness, trustworthiness, and persuasiveness [ 48 – 50 ] on visual analogue scales (" I think the presented news article was… "). Media bias awareness was established by averaging the five items and recoded to range from -5 = low bias awareness to 5 = high bias awareness ( α = .88).

Political orientation

The variable political orientation was measured on a visual analogue scale ranging from –5 = very conservative to 5 = very liberal ), introduced with the question " Do you consider yourself to be liberal , conservative , or somewhere in between ?" adopted by Spinde and colleagues [ 19 , 51 ]. Likewise, we assessed the perceived stance of the read article on the same scale introduced with the question " I think the presented news article was… ".

Attitudes towards article topic

Attitudes were assessed before and after the article presentation by a three-item semantic differential scale ( wrong - right , unacceptable - acceptable , bad - good ) evaluating the two topics (" Generally , laws restricting abortion/ the use of guns are . . ."; α = .99). The three items were averaged per topic to yield a score from (–5 = very conservative attitude to 5 = very liberal attitude). Besides, we assessed topic involvement by one item before the article presentation (" To me personally , laws restricting the use of guns/ abortions are… irrelevant-relevant") on a scale from –5 to 5.

Statistical analysis

To test effects of the visual aids on media bias perception, we used ANOVAs with effect coded factors in a forewarning message (yes/no) x annotations (yes/no) x political map (yes/no) x2 article position (liberal/conservative) x2 article topic (gun law/abortion) between design. For analyses testing political ideology effects, this was generalized to a GLM with standardized political orientation as an additional interacting variable followed by a simple effects analysis. The same model was applied to the second attitude rating, with first attitude rating and topic involvement as covariates for attitude change. This project and the analyses were preregistered with the DOI https://osf.io/e95dh/?view_only=d2fb5dc2d64741e39‌3b30b9ee6cc7dc1 (Non-anonymous Link is made accessible in case of acceptance). All study materials, code, and data are available there.

Manipulation check and other effects on perceived political stance of the article

Overall, the positions of the political articles were perceived as designed ( article position : F (1, 953) = 528.67, p < .001, η p 2 = .357): Articles assigned a liberal position were perceived more liberal ( M = 1.60, SD = 2.70), whereas conservative articles were rated more conservative ( M = –1.98, SD = 2.26). This difference between the conservative and the liberal article was more pronounced, when a forewarning message ( F (1, 953) = 7.33, p = .007, η p 2 = .008), annotations ( F (1, 953) = 3.96, p = .047, η p 2 = .004), or the political classifications were present ( F (1, 953) = 9.12, p = .003, η p 2 = .009; see Fig 3 ). The combination of forewarning and classification further increased the difference ( F (1, 953) = 5.28, p = .022, η p 2 = .006).

Across all conditions, liberal articles were perceived to be more liberal and conservative articles more conservative. The interventions increased the differences between the two ratings. Dots represent means, and lines are standard deviations.

Effects of visual aids on media bias perceptions

Testing the effects of the visual aids on media bias perceptions in general, we found that both the forewarning message ( F (1, 953) = 8.29, p = .004, η p 2 = .009) and the annotations ( F (1, 953) = 24.00, p < .001, η p 2 = .025) increased perceived bias, which we show in Fig 4 . However, we found no effect of the political classification ( F (1, 953) = 2.56, p = .110, η p 2 = .003) and no systematic higher-order interaction involving any of the manipulations ( p ≥ .085, η p 2 ≤ .003). Moreover, there were differences in media bias perceptions of the specific articles ( topic x article position : F (1,953) = 24.44, p < .001, η p 2 = .025). The two found main effects were by and large robust when testing it per item of the media bias perception scale (forewarning had no significant effect on partialness and persuasiveness) or in a MANOVA ( forewarning : F (5, 949) = 5.22, p < .001, η p 2 = .027; annotation : F (5, 949) = 6.25, p < .001, η p 2 = .032).

The forewarning message, as well as annotations, increased media bias awareness. Dots represent means, and lines are standard deviations.

Partisan media bias ratings

When considering self-indicated political orientation and its fit to the article position , we found that media bias was perceived less for articles consistent with the reader’s political orientation ( F (1,921) = 113.37, p < .001, η p 2 = .110): For conservative articles, liberal readers rated conservative articles more biased than conservative readers (β = 0.32; p < .001; 95%CI[0.25; 0.38]). Conversely, liberal articles were rated less biased by liberals (β = –0.20; p < .001; 95%CI[–0.27; –0.13]), indicating a partisan bias rating for both political isles, which we show in Fig 5 .

Bias awareness increases when the article is not aligned with the persons’ political position. Shades show 95% confidence intervals of the regression estimation.

This partisan rating of articles was unaffected by forewarning ( F (1,921) = 1.52, p = .218, η p 2 = .002), annotations ( F (1,921) = 0.26, p = .612, η p 2 < .001), and political classification ( F (1,921) = 2.72, p = .010, η p 2 = .003). Yet, with increasing liberalness of the reader, the combination of forewarning and annotation was slightly less effective on the detection of bias ( F (1,921) = 4.19, p = .041, η p 2 = .005). Furthermore, there were some topic-related differences irrelevant to the current hypotheses (higher bias was perceived for the gun laws articles ( topic : F (1,921) = 11.32, p < .001, η p 2 = .012) and specifically so for the liberal one ( topic x article position : F (1,921) = 23.86, p < .001, η p 2 = .025) with some uninterpretable minor higher order interaction ( forewarning x annotation x classification x political orientation x topic : F (1,921) = 4.10, p = .043, η p 2 = .004)).

Effects on attitudes

By and large, attitudes on the topics were not affected by the experiment: While attitudes after reading the article were in line with prior attitudes ( F (1,919) = 2415.42, p < .001, η p 2 = .724) and individual political orientation ( F (1,919) = 34.54, p < .001, η p 2 = .036), neither article position ( F (1,919) = 2.63, p = .105, η p 2 = .003) nor any of the visual aids had any general impact ( p ≥ .084, η p 2 ≤ .003). Likewise, neither of the aids interacted with the factor article position ( p ≥ .298, η p 2 ≤ .001). Solely, there were some additional minor topic-specific significant effects of the annotation combined with the forewarning ( F (1,919) = 4.77, p = .0292, η p 2 = .005) and an increased liberalness of attitude with higher topic involvement ( F (1,919) = 4.31, p = .038, η p 2 = .005), that we want to disclose, but deem irrelevant to our hypotheses and research questions.

In this study, we tested different techniques to communicate media bias. Our experiment revealed that presenting a forewarning message and text annotations enhanced awareness of biased reporting, while a political classification did not. All three methods (forewarning, annotation, political classification) impacted the political ideology rating of the presented article. Furthermore, we found evidence for partisan bias ratings: Participants rated articles that agreed with their general orientation to be less biased than articles from the other side of the political spectrum. The positive effect of the forewarning message on media bias ratings, albeit small, is in line with a few other findings of successful appeals to and reminders of accuracy motives [ 30 ]. In addition, it accords with the notion that reflecting on media bias involves some efforts [ 44 , 52 ], so motivating people to engage in this process can help detect bias.

Regarding the effects of in-text annotations, our finding differs from a previous study of a similar design [ 19 ], which did not identify the effect due to a lack of power and less optimal annotations. While news consumers may generally identify outright false or fake [ 53 ] news, detecting subtle biases can profit from such aids. This indicates that bias detection is far from ideal, particularly in more ambiguous cases. As in-text annotation and forewarning message effects were independent of each other, participants seemingly do not profit from the combination of aids.

On the other hand, the political classification could solely improve the detection of the political alignment of the text (which was also achieved by both other methods) but not help detecting biased language. Subsequently, the detection of biased language and media bias itself does not appear to be directly related to an article’s political affiliation.

Our study also replicates findings that the detection of media bias and fake news is affected by individual convictions [ 30 , 40 , 42 ]: We found that participants could detect media bias more readily if there was an incongruence between the participant’s and the article’s political ideology. Such a connection may be particularly true for detecting more subtle media biases and holding an article in high regard compared to successfully identifying outright fake news, for which a reversed effect could be found in some instances (Pennycook & Rand, 2019).

In addition, interventions were ineffective to lower such partisan effects. Similarly, attitudes remained relatively stable and were not affected by any of the visual aids. Making biased language more visible and reminding people of potential biases could apparently not help them overcome their ideology in rating the acceptance of an article when there is no clear indication that the information presented in the article is fake but solely biased. Likewise, the forewarning message successfully altered the motivation to look for biased language, but did not decrease the effects of political identity on the rating: While being able to detect the political affiliation of an article, it seems that participants were not capable of separating the stance of the article from its biased use of language, even when prompted to do so. In the same vein, effects were not more pronounced when the political classification was further visualized, potentially pointing to the notion that the stance is also detected without help (after all, while the manipulations increased the distinction between liberal and conservative articles, the article’s position was reliably identified even without any supporting material) and that partisan ratings are not a deliberate derogatory act. Furthermore, the problem of partisan bias ratings also did not increase with increased media bias awareness via the manipulations, as could have been expected by cognitive dissonance theory.

For future work, we will improve the representativeness of the surveyed sample, which limits far-reaching generalizations at this point. Additionally, we will increase the generalizability by employing articles that are politically neutral or exhibit comparatively low bias. Both forewarning and annotations may have increased ratings in this study, but it is unclear whether they also aid in identifying low-bias articles and leading to lower ratings, respectively. Improving the quality of our annotations by including more annotators is an additional step towards exhausting potential findings. We will also investigate how combinations of the visualizations and strategies work together and conduct expert interviews to determine which applications would be of interest in an applied scenario. Still, the current study shows that two of our interventions raised attention to biased language in media, giving a first insight into the yet sparsely tested field of presenting media bias to news consumers.

Furthermore, there is a great challenge in translating these experimental interventions to applications used by news consumers in the field. While forewarning messages could be implemented quite simply in the context of other media, for instance, as a disclaimer (see [ 30 ]), we hope that automated classifiers on the sentence level will prove to be an effective tool to create instant annotating aids for example as browser add-ons. Even though recent studies show promising accuracy improvements for such classifiers [ 31 , 32 ], we still want to note that much research needs to be devoted to finding stable and reliable markers of biased language. Future work also has great potential to consider these strategies as teaching tools to train users in identifying bias without visual aids. This could offer a framework for a large-scale study in which additional variables measuring previous news consumption habits could be employed.

In the context of our digitalized world, where news and information of differing quality are available everywhere, our results provide important insights for media bias research. In the present study, we were able to show that forewarning messages and annotations increased media bias awareness among readers in selected news articles. Also, we could replicate the well-known hostile media bias that consists of people being more aware of bias in articles from the opposing side of the political spectrum. However, our experiment revealed that the visualizations could not reduce this effect, but partisan ratings rather seemed unaffected. In sum, digital tools uncovering and visualizing media bias may help mitigate the negative effects of media bias in the future.

Funding Statement

This work was supported by the German Research Foundation [DFG] ( https://www.dfg.de/ ) under Grant 441541975, the German Research Foundation Centre ( https://www.dfg.de/ ) of Excellence 2117 "Centre for the Advanced Study of Collective Behaviour" (ID: 422037984). It was also supported by the Hanns-Seidel Foundation ( https://www.hss.de/ ) and the German Academic Exchange Service (DAAD) ( https://www.daad.de/de/ ). None of the funder played any role in the study design or any publication related decisions.

Data Availability

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16 The Psychology Underlying Media-Based Persuasion

Robin L. Nabi, Department of Communication, University of California, Santa Barbara, Santa Barbara, CA

Emily Moyer-Gusé, School of Communication, The Ohio State University, Columbus, OH

• Published: 28 January 2013
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Attempts at persuasion are as ubiquitous as the media often used to disseminate them. However, to explore persuasion in the context of media, we must first consider the psychological processes and mechanisms that underlie persuasive effects generally, and then assess how those strategies might apply in both traditional as well as more innovative media. This chapter overviews three dominant frameworks of persuasion (cognitive response models, expectancy value theories, and emotional appeals), along with three more subtle forms of influence (framing, narrative, and product placement) to explore how psychological theory and media effects research intersect to shed light on media-based persuasive influence.

Introduction

Attempts at persuasion are as ubiquitous as the media often used to disseminate them. Given that decades of persuasion research has documented just how challenging it can be to alter the beliefs, attitudes, and especially the behaviors of others, it is unsurprising that media strategies have evolved in response to emerging technologies to help overcome barriers to persuasion, thus yielding the modern persuasive forms of, for example, infomercials, product placement, and viral videos. But ultimately the psychological theories of how people process such messages are relatively indifferent to the messages’ particular forms. That is, despite the rapid changes in media forms and modes of transmission, the theories used to understand their effects remain largely unchanged. Thus, to explore persuasion in the context of media, we must first consider the psychological processes and mechanisms that underlie persuasive effects generally, and then assess how those strategies might apply to both traditional as well as more innovative media, with an eye toward useful avenues for theoretical advancement. Given the vastness of the topic of persuasion has been well-addressed in volumes previously (e.g., Eagly & Chaiken, 1993 ; Dillard & Pfau, 2002 ; Perloff, 2010 ), we will not attempt to offer a comprehensive review of the extant psychological research on persuasion here. Rather, we focus on the theoretical frameworks most directly linked to current research in media effects, how these theories have been applied in media contexts, and issues that might prove fruitful for future examination.

Although many definitions of persuasion exist, they tend to share several common elements. Persuasion is typically understood as a process whereby a message sender intends to influence an (uncoerced) message receiver's evaluative judgments regarding a particular object. Given media effects research tends to emphasize the unintended and often negative influence of media content on receivers, we wish to be clear that this chapter focuses exclusively on intentional effects at persuasion rather than the incidental social influence that might occur as a result of mass media exposure.

There are a number of classes of persuasion theories that focus on a range of psychological mechanisms driving influence that might be applied to the study of media effects. However, there are three theoretical orientations that have received substantial attention from media effects scholars interested in more direct and obvious attempts at persuasion, such as advertisements or public service announcements: cognitive response models, expectancy-value theories, and emotional appeals. Further, there are three additional frameworks that have been given particular attention in the context of more subtle, although arguably more powerful, forms of media-based persuasive influence: framing, narrative, and product placement. This chapter reviews the literature in each of these areas, with particular attention to how such research might evolve in response to the ever-changing media environment.

Theory Underlying Mediated Persuasive Appeals

As noted, a plethora of theories and models have been applied in media contexts, but three stand out as guiding the discussion of media influence: cognitive response models, expectancy-value theories, and fear appeals. We address each one in turn.

Cognitive Response Models of Persuasion

Cognitive response models of persuasion assume that the thoughts people have during message exposure drive their subsequent attitudes. As such, message recipients are viewed as active participants whose cognitive reactions mediate the influence of a persuasive attempt. Most notable among these models is Petty and Cacioppo's ( 1986 ) elaboration likelihood model (ELM) of persuasion, which suggests two possible routes to persuasion—central and peripheral. If sufficient processing motivation and ability are present, central processing is expected to occur during which the receiver will give thoughtful consideration to the arguments and information presented. The ratio of favorable to unfavorable cognitive responses generated about the message is then expected to predict persuasive outcome. If either processing motivation or ability is impaired, the receiver is expected to engage in peripheral processing during which simple, though not necessarily relevant, cues present in the persuasive setting will influence attitudinal response.

Petty and Cacioppo ( 1986 ) note that because greater message elaboration is expected to generate more thoughts that are then incorporated into cognitive schema, attitude change based on central processing is expected to be more stable, enduring, and predictive of behavior than attitude change based on peripheral processing. The nature and valence of the cognitive responses generated during central processing may be guided by a range of factors, including initial attitude, prior knowledge, personality factors, and mood. Moreover, they acknowledge that biased processing may occur to the extent various factors, particularly initial attitude, influence motivation or ability to process the message, resulting in more or less favorable thoughts about the object than might have been expected otherwise.

The ELM has been tested in numerous lab studies, the results of which tend to support its predictions (Eagly & Chaiken, 1993 ). However, several important theoretical and empirical criticisms have been launched against it, including the dichotomy between central and peripheral processing, the tautological definition of argument strength, and the inability to specify a priori whether particular message features will be processed centrally or peripherally (Stiff, 1986 ; Stiff & Boster, 1987 ; and responses by Petty, Cacioppo, Kasmer, & Haugtvedt, 1987 and Petty, Kasmer, Haugtvedt, & Cacioppo, 1987 ; also, see Eagly & Chaiken, 1993 ). The ELM has not been appreciably modified in light of these criticisms; however, the notion of thought confidence influencing outcomes was introduced in the early 2000s, suggesting that confidence in one's thoughts about the message intensifies their effect (i.e., confidence in favorable thoughts enhances persuasion and confidence in unfavorable ones detracts) (Petty, Brinol, & Tormala, 2002 ). However, this element of the model has seen little additional attention in the extant research since its introduction.

Chaiken's heuristic-systematic model (HSM) of persuasion offers a similar, although more clearly specified dual-processing approach. The HSM suggests that accuracy-motivated people may assess message validity through two types of message processing—heuristic and systematic—which may operate concurrently depending on the receiver's judgmental confidence threshold for a particular issue (Chaiken, 1980 , 1987 ; Chaiken, Liberman, & Eagly, 1989 ; Eagly & Chaiken, 1993 ). As cognitive misers, individuals are expected to base decisions on heuristics if they can be sufficiently confident in the accuracy of those decisions. If sufficient confidence cannot be reached using simple decision rules, individuals are expected to then also engage in the more effortful systematic processing. Although the HSM's systematic processing and the ELM's central processing are essentially the same, heuristic and peripheral processing differ in that the former is conceptualized as only cognitive and rational, whereas the latter is believed to encompass any cognitive or affective processes other than close message scrutiny. Research testing the unique aspects of the HSM has offered some evidence consistent with the model's propositions, particularly that of concurrent processing and the attenuation of heuristic effects by systematic processing (Eagly & Chaiken, 1993 ). Further, the model has been elaborated by identifying multiple motives for message processing (i.e., accuracy, defensive, and impression motivations). However, research has not directly targeted the sufficiency threshold construct, thus limiting insights into the factors that might move the threshold higher or lower, which would have implications for the information needs of the audience.

Given that media effects scholars have generally adopted the view of audiences as active consumers of messages rather than mere passive information recipients, it is understandable why cognitive response models have been readily embraced by media effects scholars. Indeed, the ELM and HSM have been applied in numerous traditional advertising contexts, including those related to health (e.g., Wilson, 2007 ; Smith, Lindsey, Kopfman, Yoo, & Morrison, 2008 ), politics (e.g., Holbert, Garrett, & Gleason, 2010 ), and of course commercial products (e.g., Whittler & Spira, 2002 ). Generally speaking, such research tends to apply these theories to understand how various features of the audience (e.g., motivation) and features of the messages (e.g., arguments and cues) interact to lead to changes in attitudes, behavioral intentions, and behaviors.

There is a wealth of evidence supporting the tenets of cognitive-response models in advertising contexts. However, perhaps because they were developed with a focus on psychological mechanisms rather than message design, they are not particularly responsive to the complexities with which modern media messages may be presented. For example, whereas the majority of ELM-based studies are based on text-only messages, an overwhelming proportion of mediated persuasive messages contain visuals. According to Petty and Cacioppo's ( 1986 ) arguments, the persuasive impact of visuals would depend on whether the receiver is motivated and/or able to process the message. If motivated and able, the visuals will be taken as arguments. Otherwise, they will be used as cues and have ephemeral effects on attitudes. However, given visuals (unlike text) can be processed quickly with minimal cognitive effort, one might have low motivation and ability and yet still be greatly impacted by a particularly gripping image that can be processed nearly automatically and, in turn, result in long-lasting attitude change. Thus, the ELM, in its current form, seems somewhat insensitive to more modern persuasive contexts.

As the design of persuasive media messages evolves, some research will certainly continue to work within the typical framework of dual-processing cognitive response models, examining, for example, how innovative message features, like interactive social agents (e.g., Skalski & Tamborini, 2007 ) and online reviews (Lin, Lee, & Horng, 2011 ), influence processing motivation or serve as peripheral cues. However, given the ELM and HSM were developed largely in the context of more text-based, expository messages—a less typical form of media presentation in recent years—some fundamental assumptions about the nature of message processing as captured by these theories, may be challenged by newer media formats. For example, Cho ( 1999 ) articulated a modified elaboration likelihood model to address the processing of Web advertising, arguing for the roles of both voluntary and involuntary ad exposure as well as the mediating effects of repeated exposure, attitude toward the site, and attitude toward Web advertising, beyond the roles of processing motivation and ability as articulated by the ELM. San Jose-Cabezudo, Gutierrez-Arranz, and Gutierrez-Cillan ( 2009 ) also argue that how Web pages are presented influences the nature of the information processing that ensues.

In sum, cognitive response models have provided a very useful framework for understanding how media messages may lead to persuasive effects, and will surely continue to guide examination of unique media features in the coming years. However, newer media forms may bring to light limits of these theories developed in an era of less complex message design and thus ideally generate theoretical innovations sensitive to these technological changes.

Expectancy Value Theories

A second class of persuasion theories—expectancy value theories—also focuses on cognition, but these theories assume audiences are rational decision makers who weigh the pros and cons of their options. More specifically, they assume people have expectancies regarding whether an object has a certain attribute, and they ascribe a particular value to that attribute. In combining these assessments, one's attitude is formed. Indeed, it was the endeavor to understand the conditions under which stable attitude–behavior relationships could be found that resulted in the development of the most well-known expectancy-value theory—the theory of reasoned action (TRA) (Fishbein & Ajzen, 1975 ), one of the more influential theories of social influence in the last 50 years.

According to the TRA, the best predictor of volitional behavior is behavioral intention (Fishbein & Ajzen, 1975 ). Behavioral intentions, in turn, are based on two types of cognitive antecedents: (1) attitudes toward performing a particular behavior, and (2) the subjective norm surrounding that behavior. Attitudes are comprised of groups of salient beliefs regarding behavioral outcomes and evaluations of those outcomes. Comparably, the subjective norm is comprised of perceptions of important others’ attitudes regarding one performing the behavior and motivation to comply with their opinions. Under this conceptualization, other variables, like demographics, personality traits, and related attitudes, affect behavior only insofar as they affect the individual's beliefs, evaluations, or motivations to comply. A meta-analysis of TRA-based research supports the model's propositions that attitudes and subjective norms can accurately predict behavioral intentions and, in turn, behaviors (Sheppard, Hartwick, & Warshaw, 1988 ). In addition, a meta-analysis of 138 attitude–behavior correlations further supports the TRA's position that attitudes can have strong associations with behaviors across a range of topics (Kim & Hunter, 1993 ).

Despite the wealth of evidence supporting the TRA, its critics argue that its utility and predictive ability are limited by its intended applicability to: (1) volitional behaviors only, (2) stable attitudes and behavioral intentions, and (3) corresponding attitude and behavior measures in terms of target, context, time, and action (see Eagly & Chaiken, 1993 for a critical review). In fact, several individual and situational barriers have been identified as having a significant impact on the translation of attitudes and/or intentions into behaviors, including time, money, the cooperation of other people, and personal self-efficacy (Triandis, 1977 ; Ajzen, 1985 ; Ajzen & Madden, 1986 ; Madden, Ellen, & Ajzen, 1992 ). Indeed, the theory of planned behavior was developed to help address these limitations (Ajzen, 1985 ), and recently Fishbein and Ajzen ( 2010 ) have elaborated on the critical elements of the reasoned action perspective, including the origins of beliefs, the role of injunctive versus descriptive norms, and the determinants of perceived behavioral control. Still, within its self-identified boundaries, TRA-based research has generated evidence to demonstrate that under the appropriate circumstances, attitudes can reliably predict behavioral intentions and behaviors.

Applied to media-based persuasion, the TRA is most helpful in suggesting what message content, rather than message design features, might best produce persuasive effects. To the extent a behavior is more heavily influenced by attitudes, one might attempt to change already-held outcome–belief expectancies or the valuations ascribed to those outcomes. Or one might look to add new belief clusters to the attitude equation. If the subjective norm is more dominant in predicting behavioral intentions and behaviors, then producing messages that speak to perceptions of what others think, motivation to comply, or adding new important others to the equation may be effective. Moreover, one might also attempt to alter the weighting of the attitude relative to the subjective norm to affect shifts in behavioral intentions and behaviors. Although this framework is extremely useful in guiding conceptually what one might hope to achieve with a persuasive message, the TRA is silent on how one might actually implement those ideas in message design. Further, the TRA is very limited in its consideration of factors beyond “rational” beliefs. Most notably, the role of emotion is not incorporated into the model in any direct or meaningful way. Given emotion (as described in a later section) is a primary motivational force underlying behavior, this is a very notable limitation of the TRA.

As media message platforms shift such that persuasive messages may be easily avoided (e.g., fast forwarding through commercials or blocking pop-up ads on web sites) or alternatively hard to avoid (e.g., embedded in web sites of interest), it has become increasingly important to take into consideration how belief-based information is presented to capture attention. Yet, it is this very presentation that may shift audiences away from more rational and deliberative decision making (see discussions of emotion and framing that follow). Although Fishbein and Ajzen ( 2010 ) would likely argue that such message features (e.g., emotional presentations) act merely as background variables influencing behaviors only indirectly through the beliefs formed, the action tendencies associated with emotions generated from media presentations may actually serve to intensify the likelihood of action without full mediation through beliefs. Thus, important directions for future research will be to consider not just how various message features may influence the construction of attitudes and subjective norms via information salience, but also how the process of influence through to behavior is influenced by the psychological state the audience may be in because of those attention-getting contextual features.

Emotion and Persuasion

A third dominant framework for media-based persuasion research focuses on affective states. Most of the research here has centered around fear arousal and its effects on both message processing and persuasion-related outcomes (e.g., attitudes, behavioral intentions, and behaviors), although the persuasive influence of other emotional states is receiving increasing attention.

Fear Appeal Research

The fear appeal literature has cycled through several theoretical perspectives over the past 50 years (see Nabi, 2007 for more detailed discussion), including: (1) the drive model, which conceptualized fear as resembling a drive state, motivating people to adopt recommendations expected to alleviate the unpleasant state (e.g., Hovland, Janis, & Kelley, 1953 ); (2) the parallel processing model (PPM) (Leventhal, 1970 ), which separated the motivational from the cognitive aspects involved in processing fear appeals, suggesting that those who respond to fear appeals by focusing on the threat (cognition) would engage in adaptive responses, whereas those responding with fear (emotion) would engage in maladaptive responses; (3) the expectancy value–based protection motivation theory (Rogers, 1975 , 1983 ), which ultimately focused on four categories of thought generated in response to fear appeals—judgments of threat severity, threat susceptibility, and response and self-efficacy—and how they might combine to predict message acceptance; and (4) the extended parallel process model (EPPM) (Witte, 1992 ), which integrated the PPM and PMT, predicting that if perceived efficacy outweighs perceived threat, danger control and adaptive change will ensue. If, however, perceived threat outweighs perceived efficacy, then fear control and maladaptive behaviors are expected.

Although meta-analyses of fear research essentially suggest that the cognitions identified in the PMT, and later the EPPM, are important to fear appeal effectiveness, no model of fear appeals has been endorsed as accurately capturing the process of fear's effects on decision making and action (see Mongeau, 1998 ; Witte & Allen, 2000 ). Regardless, evidence does support a positive linear relationship between fear and attitude, behavioral intention, and behavior change. Thus, to the extent message features evoke perceptions of susceptibility and severity, as well as response and self-efficacy, fear may moderate persuasive outcome, although there are still important questions about the interrelationships among these constructs that remain unanswered. Further, questions about whether severity and susceptibility information should always be included in fear appeals or whether “implicit” fear appeals might be more effective have also been raised (Nabi, Roskos-Ewoldsen, & Dillman Carpentier, 2007 ). Thus, there is still much work to be done in linking the theory of fear appeals to appropriate message design.

Beyond Fear Appeals

There is growing interest, as well, in understanding the effects of emotions other than fear in the processing of persuasive messages (see Nabi, 2007 for a more extensive discussion), and emerging models attempt to examine those processes. For example, the cognitive functional model (CFM) (Nabi, 1999 ) attempts to explain how message-relevant negative emotions (e.g., fear, anger, sadness, guilt, disgust) affect the direction and stability of persuasive outcome based on three constructs—emotion-driven motivated attention, motivated processing, and expectation of message reassurance. An initial test of the model (Nabi, 2002 ) offered support for some, although not all, of the model's propositions, but as it awaits future tests, the CFM offers insight into the process through which a range of discrete emotions, not just fear, influences message processing, and outcomes.

In a similar vein, Nabi ( 2003 , 2007 ) posits an emotions-as-frames model to explain the effects of more general media exposure on attitudinal and behavioral outcomes. In this model, emotions are conceptualized as frames, or perspectives, through which incoming stimuli are interpreted. The model first notes the message features likely to evoke various discrete emotions. These emotional experiences, moderated by individual differences (e.g., schema development, coping style), are predicted to influence both information accessibility and information seeking that ultimately generate emotion-consistent decisions and action. Nabi argues that through this perspective we may develop a better understanding of the central role emotions may play in understanding how frames in a range of media messages, including those designed to persuade, might impact attitudes and behaviors.

Notably missing from this discussion thus far, however, is research on the persuasive effect of positive emotional states. There has been a fair amount of attention to humor; however, reviews of the humor literature have concluded that though humor may enhance message attention and source liking, it is generally no more persuasive than nonhumorous messages (see Weinberger & Gulas, 1992 for a more nuanced discussion). With the increasing popularity of political satire programs, such as the Daily Show and the Colbert Report , there has been an upswing in interest in examining the process through which humor may have persuasive influence. For example, Nabi, Moyer-Gusé, and Byrne ( 2007 ) argue that humor may not have immediate persuasive effect because although audiences attend closely to the message, they discount it as a joke that is not intended to persuade, thus minimizing the message content's effects on their attitudes. However, they also posit that this type of processing may lead to a “sleeper effect” such that the persuasive effect of humor may emerge after some time has passed (see also Young, 2008 ). Clearly, future research would benefit from closer examination of the persuasive effect of positive emotions like hope and pride and the processes through which such effects might emerge.

Beyond the types of emotions investigated, future research would also benefit from greater attention to issues such as the role of emotion in overcoming barriers posed by selective attention. In the cluttered media environment, garnering attention to one's persuasive message is increasingly challenging and yet a necessary (although not sufficient) step in the persuasion process. Emotional appeals may be particularly well-suited to this task. Of course, once attention is gained, it is critical that the rest of the message be structured appropriately for the target audience. Risk of boomerang effects due to psychological reactance (message resistance stemming from perceiving an unjust restriction on one's freedom to choose) (Brehm, 1966 ), denial, problem minimization, and the like persist. Understanding the delicate balance between gaining attention and harnessing it to intended effect has been an elusive challenge in the area of emotional appeals and persuasion, and future research would be well served by tackling this difficult message design issue.

Further, understanding in greater detail how the specifics of message content may relate to an audience's emotional state is a surprisingly understudied issue. For example, the assumption in fear appeal research is that people are scared by threats to their physical body, most especially thoughts of death. Yet fears of disability (e.g., paralysis, blindness) or disfigurement may prove equally, and sometimes more, frightening than death. Further, not all fears are rooted in physical well-being. Evidence suggests that younger audiences’ assessments of their likelihood of experiencing a range of health problems are characterized by the optimism bias, and compared with adults, teenagers minimize the perceived risk associated with the occasional involvement in health-threatening acts (Cohn, Macfarlane, Yañez, & Imai, 1995 ). However, given that teenagers and young adults are still forming their identities, threats to social acceptance may be far more salient and thus more frightening to such audiences than threats to physical well-being. Thus, social harm–based fear appeals may be more effective for young audiences than physical harm–based fear appeals. Future research would be well served by considering not simply a greater range of emotional responses, but also the matching of message content and features to the intended emotional arousal for particular target audiences and by continuing to explore the impact of discrete emotional arousal states on information processing and decision making.

Subtler Forms of Persuasion

In addition to the three dominant frameworks of persuasion that have been examined in media contexts, there are three other, subtler forms of persuasion that warrant close attention: framing, narrative, and product placement.

Framing theories generally posit that the way in which information is presented, or the perspective taken in a message, influences a range of audience responses. As Entman ( 1993 ) states, a message frame will “promote a particular problem definition, causal interpretation, moral evaluation, and/or treatment recommendation….” (p. 52). As a result of the framing process, receivers notice, process, think about, and store information in a manner consistent with a particular message frame (Fiske & Taylor, 1991 ), which in turn may influence the information people use to form opinions (Price & Tewksbury, 1997 ). In essence, a frame is a perspective infused into a message that promotes the salience of selected pieces of information over others. When adopted by receivers, frames may influence individuals’ views of problems and their necessary solutions.

Several prominent studies provide evidence supporting this claim in a variety of contexts, including how language choice influences risky decision-making (Kahneman & Tversky, 1984 ), how television news framing affects attributions of responsibility for both the causes of and the treatments for social problems (Iyengar, 1991 ), how political journalistic news norms help to define the ideas people express when talking about politics (Gamson, 1992 ), and how news coverage of political campaigns influences how the public thinks about political processes (Cappella & Jamieson, 1997 ). In sum, these research programs indicate that the way in which information is presented can influence how people understand, evaluate, and act on a problem or issue.

Regarding the cognitive processes through which framing effects occur, it is generally suggested that such effects are the result of information accessibility biases. According to Iyengar ( 1991 ), when fed a steady diet of one type of frame over another, individuals tend to recall and use the information consistent with the predominant frame when making a decision. Price and Tewksbury ( 1997 ) argue that such accessibility of applicable information from memory influences decision making in both the short and long term if those thoughts continue to be made accessible to the individual by repetitious exposure to certain frames over others. Cappella and Jamieson ( 1997 ) also argue that news frames stimulate access to certain information, beliefs, and/or inferences, making them increasingly accessible with repeated exposure. However, they further suggest that decision making is influenced by both memory-based and online processing rather than relying solely on memory-based influences.

There is great diversity in the rich extant framing literature in terms of conceptualization, operationalization, and context application, and the many calls for strong theory development to help make sense of this increasingly disparate area of influence have been generally unsuccessful. Still, there are helpful ways to view the framing literature that give it some clearer structure. In considering the various ways in which framing has been studied, Shah, McLeod, Gotlieb, and Lee ( 2009 ) identify two key dimensions—precision versus realism, and context-specific frames versus context-transcendent frames. Precision involves holding information content constant while manipulating only the way in which that information is presented, whereas realism allows for natural variation in messages (including their content) to allow for greater external validity. This is a critical distinction because framing effects based on precision can be argued to be solely a function of the presentation of the information, whereas framing based on realism, although perhaps more externally valid, conflates influence based on both content and style.

Regarding Shah et al.'s second dimension, context-specific research focuses on the frames that may exist within a certain topic that do not translate to other contexts or when applied to another context do not mean the same thing (e.g., an economic frame in health care is not the same as an economic frame in a political campaign), whereas context-transcendent views suggest that there are more generic forms of presentation that cut across contexts (strategy versus issue frames in politics or gain versus loss frames in health). These dimensions certainly highlight critical ways in which framing studies may vary, although the field still awaits more detailed theorizing building on these variations.

Given all media messages (and indeed all messages generally) involve selection and salience by virtue of their mere construction, framing is an inherent part of all persuasive messages. The question most central to this chapter, then, is what forms of media message construction result in what patterns of influence. As we look to the communication literature, framing is a central feature of research in both political and health communication. The political communication research tends to focus more on unintentional effects of the news media's presentation of issues, whereas health communication research considers more directly intentional efforts to influence audiences’ attitudes and behaviors, so it is on the latter area that we will focus.

The bulk of research on framing, persuasion, and health is rooted primarily in prospect theory. Prospect theory is a particular brand of framing theory developed to explain choices involving risk in which people often prefer options incompatible with the most rational choice (Kahneman & Tversky, 1979 , 1984 ; Tversky & Kahneman, 1981 ). More specifically, messages inviting risky decision making may present issues in terms of one of two basic frames: losses (e.g., disadvantages or detriments related to not engaging in an advocated behavior) or gains (e.g., benefits or advantages related to complying with an advocated behavior). In processing the framed options, message receivers subjectively evaluate them, assessing value based on whether they are expected to improve or impair the current situation. Importantly, this subjective perception is often at odds with the objective likelihood of experiencing an outcome.

Consequently, according to Tversky and Kahneman ( 1981 ), there are predictable shifts of preference depending on whether a problem is framed in terms of gains or losses. In general, people are more risk averse when faced with gain-framed messages. That is, if a person sees the current situation as good or certain, she or he will hesitate to engage in a behavior perceived as potentially interfering with the current status of certainty. Conversely, people are predictably more risk-seeking when exposed to loss framed messages. That is, if a person perceives the present situation as costly, she or he will feel there is little to lose by engaging in a behavior that involves some risk.

Prospect theory asserts that generally speaking, loss frames are more effective at influencing behavior (Kahneman & Tversky, 1979 ). However, Rothman and Salovey ( 1997 ) note that when assessing the impact of frame type in the context of health messages, the type of behavior (e.g., prevention or detection) is relevant. More specifically, they argue that health prevention behaviors (e.g., exercise, teeth brushing, sunscreen use) are generally viewed as less risky compared with detection behaviors (e.g., cholesterol screening, dental visit, dermatological skin check) because by nature they provide more certain and desirable outcomes, helping to maintain current health and inhibit unwanted health problems. Thus, Rothman and Salovey argue that gain frames, which also focus on a behavior's health benefits, can be more effective than loss frames in the prevention behavior context, as evidenced by research promoting regular exercise (Robberson & Rogers, 1988 ), intentions and behaviors to wear sunscreen (Rothman, Salovey, Antone, Keough, & Martin, 1993 ; Block & Keller, 1995 ; Detweiler, Bedell, Salovey, Pronin, & Rothman, 1999 ), and obtaining a plaque-fighting mouthwash (Rothman, Martino, Bedell, Detweiler, & Salovey, 1999 ).

Conversely, because of the potential to be informed of an undesirable health state, detection behaviors are often considered risky and anxiety-evoking (Rothman & Salovey, 1997 ). Because loss frames generally lead to greater adherence in risky situations, loss-framed messages are thought to be more effective in promoting detection behaviors (Rothman et al., 1993 ; Banks et al., 1995 ), such as breast self-examinations (Meyerowitz & Chaiken, 1987 ); getting a mammogram (Banks et al., 1995 ); and dental exams (Rothman et al., 1999 ; see Rothman & Updegraff, 2011 , for a current overview).

Although the relative advantages of the prevention-gain frame/detection-loss frame pairings are generally supported across individual studies, it is important to acknowledge critical potential moderators of these relationships. First, the predicted interaction is contingent on the targeted behaviors being perceived as relatively safe (prevention) versus relatively risky (detection). It is of course possible that objectively defined prevention or detection behaviors may be viewed as risky or safe, respectively, by specific audiences, thus disrupting the expected pattern of findings (Rothman & Updegraff, 2011 ). Second, a recent meta-analysis has called the strength of the detection-loss frame effect into question (O'Keefe & Jensen, 2009 ), suggesting the nature of the detection behavior itself (e.g., its ability to be cognitively or affectively engaging) may impact whether a particular frame offers persuasive advantage (see also Rothman & Updegraff, 2011 ). Third, personality traits or dispositions of the message receiver (e.g., promotion or prevention focus, approach or avoidance tendencies, regulatory focus) may influence the likelihood of framing effectiveness. For example, promotion-focused people, who are oriented toward growth and accomplishment (i.e., “wants”), may respond more favorably to gain-framed messages, whereas prevention-focused people, who are concerned with safety and security goals (i.e., “oughts”), may respond more favorably to a loss-framed message (Rothman, Wlaschin, Bartels, Latimer, & Salovey, 2008 ; Rothman & Updegraff, 2011 ). Further, additional audience factors, such as issue involvement, may influence frame effectiveness. Indeed, evidence suggests that issue involvement may lead to stronger framing effects (Maheswaran & Meyers-Levy, 1990 ; Rothman et al, 1993 ) perhaps because of closer information processing (Rothman & Salovey, 1997 ).

From a persuasion perspective, it is clear that the intentional framing of messages may have advantages to influencing attitudes, behavioral intentions, and behaviors regarding targeted health behaviors, and that a match between the audience's construal of the behavior and the message frame is critical to maximizing persuasive advantage. Understanding in more depth the factors relevant to such matching is obviously critical to harnessing framing for maximum benefit. Also greatly needed is a deeper understanding of the psychological process through which framing effects emerge (Rothman & Updegraff, 2011 ). Particularly overlooked, in our view, has been the role of emotion in these processes (Nabi, 2007 ). Apart from some general associations drawn between loss-framed messages/detection behaviors, gain-framed messages/prevention behaviors, and negative and positive feelings, respectively, there is little discussion of the role of emotion in framing effects. Deeper exploration into the ways in which specific emotions evoked within these contexts impact audience processing and decision making could be enlightening.

Also, with due respect to the extensive research on gain and loss frames in health messages, there are other forms of health message presentation that may be worth pursuing to better understand how message framing may influence health decision making. For example, as noted earlier, Nabi ( 2007 ) presents an emotions-as-frames model in which she argues that emotions themselves serve as frames to influence information accessibility and information seeking such that emotion-consistent behaviors result. Tests of these assumptions would help shed light on how different emotion frames, apart from or perhaps in conjunction with gain and loss frames, may influence health decision-making. Another context-transcendent frame set that might warrant greater attention is personal responsibility versus societal or environmental influence, in which the former frame may lead to more individual action, whereas the latter may lead to changes in public policy. Exploring the alternative ways in which health information may be presented, other than via gain versus loss, may allow for richer and more extensive applications of intentional message framing.

Finally we wish to make two additional observations. First, framing and health research tends to focus on how information is presented in text rather than how information might be presented visually or interactively. Research linking more innovative modes of information presentation to the framing literature may prove insightful. Second, there is little if any research or discussion on potential boomerang effects in the context of framing and health messages. Given the importance of understanding not simply when messages work, but also when they backfire, this line of inquiry, too, would be of great benefit, especially in light of the repeated exposure that is the goal of health campaigns.

Narrative Persuasion

Narrative persuasion offers a second common form of “subtle” influence. Although many definitions have been used, a narrative can be defined as a story with, “…an identifiable beginning, middle, and end that provides information about scene, characters, and conflict; raises unanswered questions or unresolved conflict; and provides resolution” (Hinyard & Kreuter, 2007 , p. 778). A narrative structure can offer some advantages over more overt persuasive appeals for several reasons. First, narratives are able to attract attention in a competitive media environment. Indeed, scholars have noted the unique potential for entertainment media to reach audiences compared to traditional persuasive messages or news media (Montgomery, 1990 ; Jin, 2006 ). Because entertainment narratives are appealing and engrossing, individuals are more likely to direct their attention to them, and by default, to the embedded educational message (Bandura, 2004 ). Second, stories featuring well-liked or attractive characters are well-suited for modeling behavior (e.g., Bandura, 1986 ). Third, narrative persuasion offers the unique potential to persuade without arousing traditional forms of resistance (e.g., reactance and counterarguing).

A growing body of research has examined how narrative media messages can influence viewers’ attitudes and behaviors across a variety of health or social issues. We begin our discussion first by elaborating on the primary context of the study of persuasion and narrative—entertainment-education—before discussing the theoretical foundations of the persuasive influence of narrative.

Entertainment-Education

One common application of narrative persuasion has been entertainment-education (EE). Generally speaking, EE refers to media programs that “entertain and educate, in order to increase audience members’ knowledge about an educational issue, create favorable attitudes, and change overt behavior” (Singhal & Rogers, 1999 , p. 9). Beginning with early programs developed in Mexico in the 1980s (Sabido, 1981 ), EE efforts have targeted a range of behaviors and topics, such as AIDS prevention, gender equity, condom use, sex education, and literacy (Sood, Menard, & Witte, 2004 ).

Generally speaking, evidence suggests that under the right circumstances, EE can be an effective form of persuasion (for review, see Singhal & Rogers, 2004 ). For example, one of the most successful documented EE efforts is “Soul City,” an entertainment media campaign in South Africa that, among other elements, features a prime time television drama series that addresses various health-related topics each year, including HIV prevention and control (Singhal & Rogers, 2001 ). Using pre- and posttest panel surveys, evaluations have shown that broadcasting of this program associated with increases in knowledge of HIV transmission and prevention, positive attitudes toward condom use, and increases in prevention behaviors (Singhal & Rogers, 2001 ; Soul City Institute, 2005 ).

Although International EE interventions are typically developed with specific health or prosocial goals in mind, in the United States, educational storylines are typically embedded into otherwise purely entertainment programming (Singhal & Rogers, 1999 ; Greenberg, Salmon, Patel, Beck, & Cole, 2004 ). Sometimes these storylines appear through the work of advocacy groups (Greenberg et al., 2004 ), whereas in other cases they are developed solely by television writers (Singhal & Rogers, 2004 ). Research generally supports the effectiveness of this sort of EE program on audience awareness and knowledge of various health issues, such as condom efficacy (based on an episode of the situation comedy Friends ) (Collins, Elliott, Berry, Kanouse, & Hunter, 2003 ), emergency contraception and human papillomavirus (based on a storyline from the prime time drama ER ) (Brodie et al., 2001 ), and HIV and cancer plotlines in various entertainment television programs (Beck, 2004 ; Hether, Huang, Beck, Murphy, & Valente, 2008 ; Wilkin et al., 2007 ). There is also evidence that EE efforts can influence viewer attitudes toward stigmatized groups, such as individuals with HIV (O'Leary, Kennedy, Pappas-DeLuca, Nkete, Beck, & Galavotti, 2007 ; Lapinski & Nwulu, 2008 ) or mental illness (Ritterfeld & Jin, 2006 ). Moreover, EE may influence viewers’ behavioral intentions. Specifically, viewers of television dramas featuring organ donation storylines were more likely to decide to donate if the drama explicitly encouraged it, particularly if viewers were emotionally involved in the narrative (Morgan, Movius, & Cody, 2009 ).

Exposure to an EE program can also prompt viewers to both seek out additional information about a topic, such as HIV (Kennedy, O'Leary, Beck, Pollard, & Simpson, 2004 ) or breast cancer screening (Wilkin et al., 2007 ), and inspire conversations about the topics embedded in the narrative (Valente, Kim, Lettenmaier, Glass, & Dibba, 1994 ; Papa et al., 2000 ; Sood, Shefner-Rogers, & Sengupta, 2006 ; Pappas-DeLuca et al., 2008 ). For example, a recent experiment showed that characters who model difficult conversations about safer sex can boost viewers’ self-efficacy and encourage them to engage in similar conversations in their own lives (Moyer-Gusé, Chung, & Jain, 2011 ). Thus, extant research supports the influence of EE efforts on a range of persuasion-related variables, including knowledge, attitudes, behavioral intentions, information seeking, and conversation.

The narrative influence strategy typically used in EE programs is not without limitations. For example, because the underlying persuasive content in a narrative is less overt, viewers are free to actively construct its meaning. As a result, EE narratives can lead to misinterpretation among some viewers (Singhal & Rogers, 2001 ). Although evidence for EE effectiveness abounds, some studies have found that these narrative-based messages are effective at influencing only a subset of viewers and may lead to boomerang effects among others (e.g., Vidmar & Rokeach, 1974 ; Wilson et al., 1992 ; Moyer-Gusé & Nabi, 2011 ). Such findings highlight the need for greater understanding of the mechanisms by which narrative influence occurs.

Theoretical Foundations of Narrative Persuasion

One frequently cited explanation for the success of EE efforts is social cognitive theory (SCT) (Bandura, 1986 ). Social cognitive theory revolves primarily around the functions and processes of observational learning (Bandura, 1986 , 2002b). That is, by observing others’ behaviors, including those of media figures, one may develop rules to guide one's own subsequent actions. More specifically, Bandura ( 1986 , 2002b) argues that observational learning is guided by four processes, which are moderated by observers’ cognitive development and skills. First, attention to certain models and their behavior is affected by source and contextual features, like attractiveness, relevance, functional need, and affective valence. Second, retention processes focus on the ability to symbolically represent the behavior observed and its consequences, along with any rehearsal of that sequence. Production focuses on translating the symbolic representations into action, reproducing the behavior in seemingly appropriate contexts and correcting for any errors based on the feedback received. Finally, motivational processes influence which symbolically represented behaviors are enacted based on the nature or valence (positive or negative) of the reinforcement. Such reinforcement may come from the feedback generated by one's own behavior, the observed feedback given to others, or internal incentives (e.g., self-standards). As observational learning occurs via symbolic representations, the effects are believed to be potentially long-lasting.

Because of humans’ capacity to think symbolically, mediated narratives are especially useful vehicles to achieve observational learning and increase self-efficacy to perform given behaviors (Bandura, 2001 ). Drawing from the above theoretical explication, SCT suggests that, in essence, for mediated content to positively affect audience members’ behaviors, the audience must pay attention to attractive or similar models performing relevant behaviors. Models engaging in positive behaviors should be positively reinforced, whereas those engaging in negative behaviors should be negatively reinforced (Austin & Meili, 1994 ; Bandura, 2001 ). Thus, a character who is rewarded for his or her behavior serves to positively motivate and reinforce that behavior in the mind of the viewer, whereas behaviors for which characters are punished are negatively reinforced (Bandura, 2004 ). Bandura further argues that identification with a character (the process of temporarily taking on the role of that character) and perceived similarity may enhance this effect.

Beyond the potential for modeling, more recent theorizing in narrative persuasion has pointed to the ways in which narratives may reduce message resistance. First came the notion of transportation , or absorption into a story such that one loses track of the real world and experiences the unfolding events in the story (Gerrig, 1993 ; Green & Brock, 2000 ). Transportation is argued to minimize resistance to persuasion as the audience's focus on the plot reduces their motivation and/or ability to counterargue the message position. Building from this notion, Slater and Rouner ( 2002 ) proposed the extended elaboration likelihood model (E-ELM), which, although based on the ELM (described in a preceding section), was developed specifically to address how individuals process narrative messages. The E-ELM posits that, compared with more overt persuasive appeals, narrative messages foster greater absorption and identification with characters, both of which suppress counterarguing with the underlying persuasive subtext. This enhanced state of engagement is dependent upon the appeal of the storyline, the quality of production, and the “unobtrusiveness of persuasive subtext” (Slater & Rouner, 2002 , p. 178). In this way, using a narrative structure may lead to effective persuasion by reducing viewers’ motivation to generate counterarguments while viewing.

There is certainly evidence that transportation into mediated entertainment messages relates to story-consistent attitudes (e.g., Kennedy, Turf, Wilson-Genderson, Wells, Huang, & Beck, 2011 ; Murphy, Frank, Moran, & Patnoe-Woodley, 2011 ). And although some research supports the notion that the transportation-persuasion link is a function of reduced counterargument in the context of written narratives (Green & Brock, 2000 ), little is known about the relationship between absorption and counterarguing in the context of audiovisual media (Hinyard & Kreuter, 2007 ). Indeed, evidence on this point is mixed. Slater, Rouner, and Long ( 2006 ) found that watching a television drama about controversial issues (e.g., gay marriage, the death penalty) led to greater support for related public policies with very minimal counterarguing, whereas Moyer-Gusé and Nabi ( 2010 ) found transportation positively related to participants’ self-reported counterarguing of a television drama regarding teen pregnancy. Thus, the effect of transportation on counterarguing is still somewhat of an open question.

Most recently, building on the strengths of both SCT and E-ELM, the entertainment overcoming resistance model (EORM) (Moyer-Gusé, 2008 ) focuses on how different features of media narratives (e.g., identification, parasocial interaction, perceived persuasive intent) can overcome a range of sources of persuasive resistance. For example, the EORM contends that narrative messages may reduce psychological reactance by diminishing viewers’ perception that the message intends to persuade. Similarly, including characters with whom viewers have a parasocial relationship may make the underlying persuasive content feel less authoritative, thus also reducing reactance. Further, the EORM posits that a narrative message can increase perceptions of vulnerability to some threats via identification with characters, and thus motivate positive attitude and behavior change. Indeed, Moyer-Gusé and Nabi ( 2010 ) found evidence offering support for each of these claims, although the perceived vulnerability-identification association emerged only after some time had passed, which is consistent with other research on entertainment media and sleeper effects (e.g., Appel & Richter, 2007 ; Nabi, Moyer-Gusé, & Byrne, 2007 ; Moyer-Gusé et al., 2011 ). Further, evidence suggests that identification with main characters—another form of absorption into the narrative—reduced counterarguing with the underlying message (Moyer-Gusé & Nabi, 2010 ; Moyer-Gusé, Chung, & Jain, 2011 ). In sum, a few studies have tested portions of the EORM, finding support for several predictions of the model. By linking these message-based processes to various forms of resistance to persuasion, the EORM offers insight for message designers to reduce a range of barriers to persuasive success.

In sum, considering the unique ways in which narrative media messages can overcome various forms of resistance to persuasion appears to be a useful way to understand EE effects and perhaps persuasion effects more generally. Future research should continue to explore the mechanisms of narrative persuasion effectiveness. Specifically, given that most EE research has focused on dramatic narratives, the persuasive effects of other genres, such as comedy or mystery programming, should be considered. Entertainment-education research could also benefit from a focus on identifying the right balance of entertainment and education content to maximize the effectiveness of EE messages. As noted, one limitation of EE is the potential for misinterpretation, in part stemming from the underlying persuasive message being too subtle or open to interpretation. One way this has been addressed in practice is by including an epilogue to underscore the intended message. However, little is known about how this strategy may alter EE effects. Indeed, several of the propositions of the EORM as well as the EELM depend on persuasive intent remaining in the background. Future research should examine the message features necessary to ensure that the underlying persuasive message is clear without coming across as didactic or interfering with the narrative experience.

Product Placement

Finally, we wish to address one more instance of a subtle form of persuasion in the media: product placement. Although definitions vary across the literature, product placement generally refers to the purposeful inclusion of a brand within some entertainment media content (Karrh, 1998 ; van Reijmersdal, Neijens, & Smit, 2009 ). Typically, this placement is bought or bartered by an advertiser to gain attention for a brand or to affect brand selection, and can be classified along three dimensions: visual, auditory, and plot connection (Russell, 1998 ). The visual dimension refers to the visual appearance of the brand on the screen (e.g., the number of times the product is shown, the relative size of the product on the screen, the positioning in the foreground or background). The auditory dimension addresses whether the brand is mentioned in dialog (e.g., how frequently the brand is named, emphasis placed on the brand itself, which character refers to the product). The third dimension, plot connection, involves how well-integrated the brand is into the message's storyline.

Although product placement has become increasingly popular as an advertising tactic, the research on its effectiveness is still rather diffuse and disconnected (e.g., Karrh, 1998 ; Balasubramanian, Karrh, & Patwardhan, 2006 ; van Reijmersdal et al., 2009 ). This discussion highlights the major trends in product placement research, focusing specifically on the dependent measures used to assess effectiveness, their interrelationships, and the psychological mechanisms used to explain them.

Early product placement research focused primarily on explicit memory of the placed brand as a measure of effectiveness (perhaps because of the centrality of ad recall in the advertising literature.) Explicit memory refers to what a viewer can consciously recall seeing (Law & Braun-LaTour, 2004 ), and research suggests that explicit memory is enhanced by the prominence of the product placement within the entertainment message (Gupta & Lord, 1998 ; Law & Braun, 2000 ; Yang & Roskos-Ewoldsen, 2007 ). For example, placements that are well integrated into the plot are more prominent and therefore more likely to be remembered than those that are tangential (Yang & Roskos-Ewoldsen, 2007 ). Similarly, placements are more memorable when a brand is depicted visually and verbally as opposed to just one or the other (Law & Braun, 2000 ). In these ways, more prominent placements are more likely to be explicitly remembered by audience members.

Although prominent placements are more likely to be recalled, importantly, this does not necessarily enhance brand choice. For example, Yang and Roskos-Ewoldsen ( 2007 ) in examining three levels of prominence (background, used by main character, connected to plot) found that although the more prominent placements were more readily recognized afterward, they had no effect on brand choice. In fact, explicit recall for the placement can actually lead to more negative attitudes toward the brand. Similarly, Law and Braun ( 2000 ) found that product placements that were depicted visually but not mentioned (i.e., less explicit), had a greater effect on brand choice than those that were depicted audiovisually or those that were mentioned verbally (i.e., more explicit), even though the visual-only placements were least likely to be explicitly recalled. That is, the least memorable placement was the one that most influenced brand preference.

Similar results have been found regarding connection to plot and placement within highly liked programs. Russell ( 2002 ) found that when a placement seemed out of place (such as a verbal reference to a product that is not well integrated into the plot), it was more likely to be recalled but less likely to enhance persuasion, perhaps because such placements are perceived by the audience as unnatural. Relatedly, Cowley and Barron ( 2008 ) found that viewers high, but not low, in program liking experienced less supportive attitudes in response to product placement within that program, arguably because fans are likely to pay more careful attention to the show, making them better able to explicitly recall the placement and be more conscious of the persuasive intent.

Taken together, these results show that more memorable placements do not necessarily translate into greater persuasion and indeed may interfere with persuasive effect. This apparent disjuncture may be explained based on the constructs of awareness of persuasive intent and cognitive resistance. More specifically, a prominent placement is more likely to activate viewers’ cognitive defenses against persuasive messages. When a viewer notices a placement and elaborates on its persuasive intent, it may induce greater irritation and/or counterarguing, thus leading to greater resistance (e.g., Friestad & Wright, 1994 ). More prominent placements, because they are more likely to be noticed, can lead to this sort of elaboration and counterarguing. In other words, it is because the audience notices the placement that they are able to resist its influence. Conversely, if the audience is exposed to the product placement in more subtle ways (e.g., visual depiction only, brief screen time/mention, smooth integration into the plot), the product has been primed and thus made cognitively accessible to the viewer, but because of the limited awareness of the source of its activation, the viewer is less motivated and/or able to control its influence on their choices. Thus, by increasing the cognitive accessibility of a brand, more subtle product placements may increase the likelihood that it will be chosen. Indeed, research supports this notion that exposure to a placement increases brand accessibility (e.g., Yang, Roskos-Ewoldsen, Dinu, & Arpan, 2006 ; Yang & Roskos-Ewoldsen, 2007 ).

In essence, more prominent product placements increase the likelihood that an audience member will be aware of the persuasive intent and thus be more motivated and/or able to resist in terms of product choice. Conversely, less prominent placements, although less likely to be recalled, can lead to greater product selection because of cognitive priming and accessibility. Future research would certainly benefit from comparing the relative effectiveness of product placement relative to traditional advertising forms. In addition, understanding more clearly the conditions under which product placement influences attitudes and behaviors would be useful. Further, it would be helpful to consider more directly the way in which media features and processes influence product placement effectiveness. For example, does transportation encourage or impede a product's perceived prominence within a story and thus, in turn, influence the persuasive impact of the product placement? Additionally, some research has shown that when brands are used by liked characters, they are evaluated more favorably (d'Astous, & Chartier, 2000 ; Yang & Roskos-Ewoldsen, 2007 ). Given what is known about social cognitive theory, might liked characters’ use of products increase audience liking for those products such that product prominence in the message might turn out to be a productive, rather than counterproductive, factor? Similarly, does liking a movie translate into liking products placed within them in ways that undermine the resistance associated with awareness of product placement? In sum, considering more directly the media effects variables relevant to the entertainment context together with the psychological process associated with product placement should allow both lines of research to move forward in meaningful ways.

Clearly a wide range of psychological perspectives have been applied to the context of media-based persuasion. Some of these perspectives (e.g., cognitive response models, expectancy value models, fear appeals) capture quite well the influence process associated with overt persuasive attempts in a very wide range of contexts, whereas others (e.g., framing, narrative, product placement) help capture the subtleties associated with persuasive messages conveyed in more specific media forms or contexts. And combined, both sets of literature make it quite clear that understanding what is necessary for successful persuasion requires understanding (and avoiding) the conditions of resistance to persuasion. Thus, future research will benefit from attention to the structure and design of messages that foster attention without simultaneously triggering psychological resistance. Closer consideration of issues related to emotional arousal, visual communication, transportation, and identification across these various contexts, we believe, will be critical to understanding this delicate balance. Especially given the extraordinary technological developments that are now influencing the creation, conveyance, and receipt of mediated persuasive messages, it is essential that scholars consider more fully the ways in which message construction interacts with psychological orientation to impact information processing and decision making. The marriage of psychological theory and media effects has been a fruitful one to this point, and their continued interrelationship will be critical to understanding more fully persuasion processes in the new media age.

Questions for Future Research

How will the assumptions of the ELM and other cognitive response models hold up in light of new forms of persuasive message design and delivery?

How does the construction of media-based persuasive messages designed to capture attention influence expectancy value–based calculations?

Under what conditions are positive emotional appeals effective and how do they compare with the effectiveness of negative emotional appeals?

What factors moderate the effectiveness of gain versus loss frames?

What entertainment message features link to what psychological states such that resistance to persuasion is minimized?

How does product placement effectiveness compare with the effectiveness of traditional forms of advertising?

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ENG 190: Research and Persuasion

Bias, Defined

According to the AllSides Red Blue Dictionary :

This word has largely come to be seen as something negative - reflecting the undue influence of any sort of prejudice over our judgment or decision making. Since this influence is understood to distort that judgment and decision making, the goal is often taken for granted as to become free of bias" or "unbiased" in our assessments of the world around us.

This word "unbiased" is thus often synonymous with "objective" and "neutral." Out of this view, accusations of political bias are often made as an attack on various news networks or others presuming some kind of objectivity or neutrality - e.g., scientists and scholars.

From another perspective, some sort of standpoint or bias is an inherent and innate feature of the human mind - and something that literally cannot be "escaped" or "shelved." If that is true, some would argue this "bias against bias" may cause problems by pressing people to not acknowledge their biases and pretend they do not exist. As an alternative way to seek fairness and objectivity, diverse voices may be invited to acknowledge their biases in an open, transparent way that allows a discussion to be aware of what informs it.

It is important to be able to identify bias, especially when writing for a key audience in mind. One useful tool that can help is the Media Bias Chart , which provides a ranking system of bias level in hundreds (if not thousands) of media sources. This might be able to help you in determining a neutral source from one that may be trying to persuade. Click around and see if any of the chart positions surprise you!

Bias: It's Everywhere!

Whether we like to admit it or not, everyone has some sort of bias simply from being exposed to the beliefs and ideals of the environment in which they were raised. Taken a step further, what is now known as filter bubbles or echo chambers can limit the information that can even reach you in the virtual world. Below is a Ted Talk by Eli Pariser, who was one of the first people to recognize and name this phenomenon:

SIFT: A Way to Check Your Bias

While you're researching, practice SIFT to check the validity of a source:

• Do you know the website or source of information? What is the reputation of both the claim and the website? If you don't have that information, use the next moves to get a sense of what you are looking at. 
• What is your purpose? If you just want to repost, read an interesting story, or get a high level explanation of a concept, it probably good enough to find out whether the publication is reputable. If you are doing serious research, you might want to chase down individual claims and verify them independently.
• It's a reliable, trusted news source-go ahead and read the article.
• Don't know? Keep sifting...
• Yes? Go ahead and read the article!
• No? Seek out another source.
• Yes! Bookmark them all-you will need them later.
• No? Seek out a different article. 
• No? Track down the original source (or sources) instead!
• Yes? Go ahead and read or watch the resource and gather information.
• Yes? Go ahead and read or watch the resource and gather information. 
• << Previous: Citing Your Sources

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13.4: Bias in Writing

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• Page ID 6307

• Amber Kinonen, Jennifer McCann, Todd McCann, & Erica Mead
• Bay College Library

Everyone has various biases on any number of topics. For example, you might have a bias toward wearing black instead of brightly colored clothes or wearing jeans rather than formal wear. You might have a bias toward working at night rather than in the morning, or working by deadlines rather than getting tasks done in advance. These examples identify minor biases, of course, but they still indicate preferences and opinions.

Handling bias in writing and in daily life can be a useful skill. It will allow you to articulate your own points of view while also defending yourself against unreasonable points of view. The ideal in persuasive writing is to let your reader know your bias, but do not let that bias blind you to the primary components of good argumentation: sound, thoughtful evidence and a respectful and reasonable address of opposing sides.

The strength of a personal bias is that it can motivate you to construct a strong argument. If you are invested in the topic, you are more likely to care about the piece of writing. Similarly, the more you care, the more time and effort you are apt to put forth and the better the final product will be.

The weakness of bias is when the bias begins to take over the essay—when, for example, you neglect opposing ideas, exaggerate your points, or repeatedly insert yourself ahead of the subject by using and repeating personal pronouns such as “I” too often, such as stating. “I believe guns should be outlawed” or “I think smoking’s bad.” Excessive repetition of any word will eventually catch the reader’s attention—and usually not in a good way. The use of I is no different.

Being aware of all three of these pitfalls will help you avoid them.

Checklist: Developing Sound Arguments

Does my essay contain the following elements?

• An engaging introduction
• A reasonable, specific thesis that is able to be supported by evidence
• A varied range of evidence from credible sources
• Respectful acknowledgment and explanation of opposing ideas
• A style and tone of language that is appropriate for the subject and audience
• Acknowledgment of the argument’s limits
• A conclusion that will adequately summarize the essay and reinforce the thesis

• Share full article

Advertisement

How A.I. Chatbots Become Political

By Zvi Mowshowitz Graphics by Sara Chodosh

Mr. Mowshowitz writes a newsletter about artificial intelligence.

We increasingly rely on artificial intelligence chatbots as tools to understand the world. Some are already replacing internet search engines and aiding in other tasks like writing and programming. Keeping an eye on chatbots’ emergent behaviors — including their political attitudes — is becoming more and more important.

A.I.’s political problems were starkly illustrated by the disastrous rollout of Google’s Gemini Advanced chatbot last month. A system designed to ensure diversity made a mockery of user requests, including putting people of color in Nazi uniforms when asked for historical images of German soldiers and depicting female quarterbacks as having won the Super Bowl, forcing Google to suspend the creation of pictures of humans entirely. Gemini’s text model often refuses to illustrate, advocate or cite facts for one side of an issue, saying that to do so would be harmful, while having no such objection when the politics of the request are reversed.

The fact that A.I. systems express political leanings matters because people often adopt the views they most regularly encounter. Our politics and media are increasingly polarized . Many worry that Facebook’s, YouTube’s and TikTok’s content algorithms exacerbate ideological polarization by feeding users more of what they are already inclined to agree with and give Big Tech the ability to put its thumb on the scale. Partisan A.I. chatbots only intensify this.

How do such political preferences come about in A.I. models?

A preprint of a new paper by the machine-learning researcher David Rozado sheds new light on the question. He administered 11 political orientation tests to 24 state-of-the-art A.I. language models and found a consistent pattern: They tend to be politically left of center and lean libertarian instead of authoritarian. These leanings are reflected in their moral judgments, the way they frame their answers, which information they choose to share or omit and which questions they will or won’t answer.

Political preferences are often summarized on two axes. The horizontal axis represents left versus right, dealing with economic issues like taxation and spending, the social safety net, health care and environmental protections. The vertical axis is libertarian versus authoritarian. It measures attitudes toward civil rights and liberties, traditional morality, immigration and law enforcement.

You can try out a short quiz for yourself to see how your views compare with the A.I. models’ answers in Mr. Rozado’s study.

Are you progressive? Conservative? Libertarian? Authoritarian?

Take the World’s Smallest Political Quiz to find out — it’s only 10 questions.

Source: The Advocates for Self-Government

Access to open-source versions of A.I. models allows us to see how a model’s political preferences develop. During the initial base training phase, most models land close to the political center on both axes, as they initially ingest huge amounts of training data — more or less everything A.I. companies can get their hands on — drawing from across the political spectrum.

Models then undergo a second phase called fine-tuning. It makes the model a better chat partner, training it to have maximally pleasant and helpful conversations while refraining from causing offense or harm, like outputting pornography or providing instructions for building weapons.

Companies use different fine-tuning methods, but they’re generally a hands-on process that offers greater opportunity for individual decisions by the workers involved to shape the direction of the models. At this point, more significant differences emerge in the political preferences of the A.I. systems.

In Mr. Rozado’s study, after fine-tuning, the distribution of the political preferences of A.I. models followed a bell curve, with the center shifted to the left. None of the models tested became extreme, but almost all favored left-wing views over right-wing ones and tended toward libertarianism rather than authoritarianism.

Source: Rozado (2024), The Political Preferences of LLMs

What determines the political preferences of your A.I. chatbot? Are model fine-tuners pushing their own agendas? How do these differences shape the A.I.’s answers, and how do they go on to shape our opinions?

Conservatives complain that many commercially available A.I. bots exhibit a persistent liberal bias. Elon Musk built Grok as an alternative language model after grumbling about ChatGPT being a “woke” A.I. — a line he has also used to insult Google’s Gemini.

Liberals notice that A.I. output is often — in every sense — insufficiently diverse, because models learn from correlations and biases in training data, overrepresenting the statistically most likely results. Unless actively mitigated, this will perpetuate discrimination and tend to erase minority groups from A.I.-generated content.

But our A.I. systems are still largely inscrutable black boxes, which makes herding them difficult. What we get out of them broadly reflects what we have put in, but no one can predict exactly how. So we observe the results, tinker and try again.

To the extent that anyone has attempted to steer this process beyond avoiding extreme views, those attempts appear unsuccessful. For example, when three Meta models were evaluated by Mr. Rozado, one tested as being Establishment Liberal, another Ambivalent Right. One OpenAI model tested as Establishment Liberal and the other was Outsider Left. Grok’s “fun mode” turns out to be a Democratic Mainstay, more liberal than the median model.

Google’s Gemini Advanced, released after Mr. Rozado’s paper, appears to be farthest to the left, but in a way that presumably well overshot its creators’ intentions, reflecting another unsuccessful steering attempt.

These preferences represent a type of broad cultural power. We fine-tune models primarily by giving potential responses thumbs up or thumbs down. Every time we do, we train the A.I. to reflect a particular set of cultural values. Currently, the values trained into A.I. are those that tech companies believe will produce broadly acceptable, inoffensive content that our political and media institutions will view as balanced.

The results do not lie at the center of our national politics. Many of the motivating ideas and forces in American political thought, regardless of what you may think of them, would be seen as unacceptable for an A.I. to articulate.

A modestly left-leaning, modestly libertarian orientation feels “normal.” So does a left-leaning interpretation of what is and is not settled science, unreliable sourcing or what constitutes misinformation. Political preferences learned from those topics may then be broadly applied across the board to many other subjects as well.

If one wants to steer this process directionally, Mr. Rozado proves it is straightforward to do. He started with GPT-3.5-Turbo and rapidly created models he called LeftWingGPT and RightWingGPT (at a total training cost of about $2,000) by feeding the model a steady diet of partisan sources. For example, RightWingGPT read National Review, while LeftWingGPT read The New Yorker.

The resulting models were far more politically extreme than any publicly available model tested by Mr. Rozado. (He did not test Gemini Advanced.)

Commercial forces will pressure companies to first make chatbots be generally inoffensive and noncontroversial, and then to give their customers what they want. YouTube, Facebook and others have learned that serving up an endless stream of personalized, unchallenging content is good for business. Future A.I. chatbots will have more context about what their users are looking for and will use that context to give it to them, both out of the box and through tools like custom instructions and fine-tuning.

See how differently LeftWingGPT and RightWingGPT answer the same question

LeftWingGPT

RightWingGPT

Note: Each model was asked to answer in three sentences each time in order to keep the answers succinct.

With A.I. models, we have two opposing risks to worry about. We may have individually customized A.I.s telling us what we want to hear. Or we may increasingly hear a particular perspective favored over others, infusing that single point of view deeply into our lives while rendering conflicting thoughts harder to even consider in the first place.

In the near future, we will turn language models into agents that work toward our goals: My A.I. will talk to or negotiate with your A.I. We will outsource increasingly complex tasks to our A.I.s. It will become easier to let them make choices on our behalf and determine what information we see. As we turn over more of our decision-making to A.I.s and lose track of the details, their values could start to override our values.

We must ensure that we are shaping and commanding the more capable A.I.s of the coming years, rather than letting them shape and command us. The critical first step in making that possible is to enact legislation requiring visibility into the training of any new A.I. model that potentially approaches or exceeds the state of the art. Mandatory oversight of cutting-edge models will not solve the underlying problem, but it will be necessary in order to make finding a future solution possible.

When Your Technical Skills Are Eclipsed, Your Humanity Will Matter More Than Ever

The rise of A.I. will make soft skills even more important.

By Aneesh Raman and Maria Flynn

Noam Chomsky: The False Promise of ChatGPT

The most prominent strain of A.I. encodes a flawed conception of language and knowledge.

By Noam Chomsky, Ian Roberts and Jeffrey Watumull

A.I. Is Coming for the Past, Too

In our focus on protecting the present and future from A.I., we have forgotten about the urgent need to protect the past.

By Jacob N. Shapiro and Chris Mattmann