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Disparagement humor makes a punchline out of a marginalized group. Racist or sexist jokes, for instance, aren’t just harmless fun – psychologists find they can foster discrimination.
Q: Why did the woman cross the road?
A: Who cares! What the hell is she doing out of the kitchen?
Q: Why hasn’t NASA sent a woman to the moon?
A: It doesn’t need cleaning yet!
These two jokes represent disparagement humor – any attempt to amuse through the denigration of a social group or its representatives. You know it as sexist or racist jokes – basically anything that makes a punchline out of a marginalized group.
Disparagement humor is paradoxical: It simultaneously communicates two conflicting messages. One is an explicit hostile or prejudiced message. But delivered alongside is a second implicit message that “it doesn’t count as hostility or prejudice because I didn’t mean it — it’s just a joke.”
By disguising expressions of prejudice in a cloak of fun and frivolity, disparagement humor, like the jokes above, appears harmless and trivial. However, a large and growing body of psychology research suggests just the opposite – that disparagement humor can foster discrimination against targeted groups.
Jokes that release restraints
Most of the time prejudiced people conceal their true beliefs and attitudes because they fear others’ criticism. They express prejudice only when the norms in a given context clearly communicate approval to do so. They need something in the immediate environment to signal that it is safe to freely express their prejudice.
Disparagement humor appears to do just that by affecting people’s understanding of the social norms – implicit rules of acceptable conduct – in the immediate context. And in a variety of experiments, my colleagues and I have found support for this idea, which we call prejudiced norm theory.
For instance, in studies, men higher in hostile sexism – antagonism against women – reported greater tolerance of gender harassment in the workplace upon exposure to sexist versus neutral (nonsexist) jokes. Men higher in hostile sexism also recommended greater funding cuts to a women’s organization at their university after watching sexist versus neutral comedy skits. Even more disturbing, other researchers found that men higher in hostile sexism expressed greater willingness to rape a woman upon exposure to sexist versus nonsexist humor.
How did sexist humor make the sexist men in these studies feel freer to express their sexist attitudes? Imagine that the social norms about acceptable and unacceptable ways of treating women are represented by a rubber band. Everything on the inside of the rubber band is socially acceptable; everything on the outside is unacceptable.
Sexist humor essentially stretched the rubber band; it expanded the bounds of acceptable behavior to include responses that would otherwise be considered wrong or inappropriate. So, in this context of expanded acceptability, sexist men felt free to express their antagonism without the risk of violating social norms and facing disapproval from others. Sexist humor signaled that it’s safe to express sexist attitudes.
Who’s the target?
In another study, my colleagues and I demonstrated that this prejudice-releasing effect of disparagement humor varies depending on the position in society occupied by the butt of the joke. Social groups are vulnerable to different degrees depending on their overall status.
Some groups occupy a unique social position of what social psychologists call “shifting acceptability.” For these groups, the overall culture is changing from considering prejudice and discrimination against them completely justified to considering them completely unjustified. But even as society as a whole becomes increasingly accepting of them, many individuals still harbor mixed feelings.
For instance, over the past 60 years or so, the United States has seen a dramatic decline in overt and institutional racism. Public opinion polls over the same period have shown whites holding progressively less prejudiced views of minorities, particularly blacks. At the same time, however, many whites still covertly have negative associations with and feelings toward blacks – feelings they largely don’t acknowledge because they conflict with their ideas about themselves being egalitarian.
Disparagement humor fosters discrimination against social groups – like black Americans – that occupy this kind of shifting ground. In our study, we found that off-color jokes promoted discrimination against Muslims and gay men – which we measured in greater recommended budget cuts to a gay student organization, for instance. However, disparagement humor didn’t have the same effect against two “justified prejudice” groups: terrorists and racists. Social norms are such that people didn’t need to wait for jokes to justify expressions of prejudice against these groups.
An important implication of these findings is that disparagement humor can be more or less detrimental based on the social position occupied by the targeted groups. Movies, television programs or comedy clips that humorously disparage groups such as gays, Muslims or women can potentially foster discrimination and social injustice, whereas those that target groups such as racists will have little social consequence.
On the basis of these findings, one might conclude that disparagement humor targeting oppressed or disadvantaged groups is inherently destructive and thus should be censured. However, the real problem might not be with the humor itself but rather with an audience’s dismissive viewpoint that “a joke is just a joke,” even if disparaging. One study found that such a “cavalier humor belief” might indeed be responsible for some of the negative effects of disparagement humor. For prejudiced people, the belief that “a disparaging joke is just a joke” trivializes the mistreatment of historically oppressed social groups – including women, gay people, racial minorities and religious minorities – which further contributes to their prejudiced attitude.
Can you be ‘in on the joke’?
In addition, if one initiates disparagement humor with the positive intention of exposing the absurdity of stereotypes and prejudice, the humor ironically might have the potential to subvert or undermine prejudice.
Chris Rock is one comedian well-known for using subversive disparagement humor to challenge the status quo of racial inequality in the United States. For instance, in his opening monologue for the 2016 Academy Awards, he used humor to call attention to racism in the film industry and hierarchical race relations more generally:
I’m here at the Academy Awards, otherwise known as the White People’s Choice Awards. You realize if they nominated hosts, I wouldn’t even get this job. So y’all would be watching Neil Patrick Harris right now.
The problem is that in order for the humor to realize its goal of subverting prejudice, the audience must understand and appreciate that intention. And there’s no guarantee that they will.
There was a good-spirited intention behind it. So then when I’m on the set, and we’re finally taping the sketch, somebody on the set [who] was white laughed in such a way – I know the difference of people laughing with me and people laughing at me – and it was the first time I had ever gotten a laugh that I was uncomfortable with. Not just uncomfortable, but like, should I fire this person?
Chapelle’s intentions with his racially charged comedy were misunderstood. By lampooning the stereotype, he meant to call attention to the ridiculousness of racism. However, it became apparent that not everyone was capable of or motivated to look past Chapelle’s comic stereotypical portrayal to get his subversive intent.
One study found that people higher in prejudice are particularly prone to misinterpret subversive humor. Researchers in the 1970s studied amusement with the television show “All in the Family,” which focused on the bigoted character Archie Bunker. They found that low-prejudiced people perceived “All in the Family” as a satire on bigotry and that Archie Bunker was the target of the humor. They “got” the true subversive intent of the show.
In contrast, high-prejudiced people enjoyed the show for satirizing the targets of Archie’s prejudice. Thus, for high-prejudiced people, the subversive disparagement humor of the show backfired. Rather than calling attention to the absurdity of prejudice, for them the show communicated an implicit prejudiced norm, conveying a tolerance of discrimination.
Psychology research suggests that disparagement humor is far more than “just a joke.” Regardless of its intent, when prejudiced people interpret disparagement humor as “just a joke” intended to make fun of its target and not prejudice itself, it can have serious social consequences as a releaser of prejudice.
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A meta-analysis looks at the relationship between reading and social ability.
As the founding director of the program in the Human Dimensions of Organizations at the University of Texas, I, and my colleagues, use the humanities and the social and behavioral sciences to teach people in workplaces about people.
Within the humanities, literature plays a significant role in what we teach. Reading literature has many potential benefits, including being able to experience things within a work of fiction that you might not have a chance to experience in real life. In addition, by showing you the world through the eyes of other people, literature can give you a window into others’ thoughts or feelings.
Does that experience create increased empathy and ability to understand others?
Psychologists have begun to explore this question by asking whether reading fiction improves individuals’ sensitivity to other people’s beliefs or emotions compared to either not reading or to reading nonfiction. A paper by David Dodell-Feder and Diana Tamir in the November 2018 issue of the Journal of Experimental Psychology: General looked across 14 studies using a technique called meta-analysis to determine whether there is reason to think that reading fiction improves social abilities.
These studies generally had some groups read fiction passages. Most studies had a control group in which people read nonfiction. A few of them had a control group in which the people did not read anything. A few studies compared reading fiction to both a nonfiction and a no-reading control.
Several different measures of social ability were used. Studies looked at people’s ability to read other people’s emotions, to judge their beliefs — and false beliefs, to take other people’s perspectives, and to guess the emotions people would experience in different situations. Some of the measures were self-report measures (“How often do you…”), while others reflected performance in a task.
The authors looked at these studies but also tried to make guesses about how many unpublished studies there are likely to be in which researchers tried to get an effect of reading and failed and thus chose not to submit their paper. When researchers choose not to submit papers that have no effect of a variable, that is called the “file drawer problem.”
Overall, the authors conclude that reading fiction does appear to influence social ability: The effect is small but reliable. In addition, measures of actual performance lead to bigger effects than self-report measures.
If the effects are small, though, are they really worth paying attention to? The authors suggest (and I agree) that they are, for a few reasons:
- First, if there really is a reliable influence of reading fiction on social ability, then this opens up a productive area for further research. It is hard to recommend to researchers that they explore a phenomenon if the studies are unlikely to work.
- Second, most of these studies ask participants to read for a short period of time and then demonstrate an influence soon after. Over time, though, people who read a lot of fiction are likely to develop habits to pay attention to the kinds of information that fiction leads them to consider. So, the effects of reading over the long term are likely to be even stronger than what is observed in these studies (though that is something that future research should tackle).
For now, though, keep a good fiction book around and make reading part of your regular routine.
Dodell-Feder, D. & Tamir, D.I. (2018). Fiction reading has a small positive impact on social cognition: A meta-analysis. Journal of Experimental Psychology: General, 147(11), 1713-1727.
Art Markman, Ph.D., is a cognitive scientist at the University of Texas whose research spans a range of topics in the way people think.
Online viewers mirror the emotions of vlogs posted by YouTubers, a study finds.
Do you subscribe to any YouTube channels? Do you feel especially simpatico with a particular YouTuber? Does the emotional content of a YouTube video blog (vlog) mirror the word-emotion associations used by viewers when writing comments about the video blogger (vlogger) or vlog?
These are the kinds of questions that researchers from Tilburg University in the Netherlands were interested in when they began investigating what happens when “birds of a feather flock together” on YouTube.
Their report, “Multilevel Emotion Transfer on YouTube: Disentangling the Effects of Emotional Contagion and Homophily on Video Audience,” was published online today in the journal Social Psychological and PersonalityScience. According to the researchers, this is the first study to use a video-focused social media source such as YouTube to explore emotional contagion and homophily. The term “homophily” (McPherson et al., 2001) refers to the tendency we have to bond with people like ourselves.
We all know from firsthand experience that being around someone who is anxious can make you anxious; being around someone giddy can make you giddy; being around someone grouchy makes you grouchy; and so on. Surprisingly, until now, there’s been relatively little research on how these types of interpersonal emotional “contagions” spread online via YouTube.
For their recent investigation into emotional contagions and homophily on YouTube, a trio of researchers from Tilburg University led by Hannes Rosenbusch examined 2,083 YouTube vlogs that were selected from a pool of 110 vloggers (a.k.a. “YouTubers”) who had at least 10,000 subscribers.
Rosenbusch et al. used a word-emotion association lexicon to measure the range of emotions expressed in user comments on each particular vlog. The NRC Emotion Lexicon is a comprehensive list of English words and their associations with eight emotions—anger, fear, anticipation, trust, surprise, sadness, joy, and disgust—and, more broadly, with negative emotions and positive emotions.
“We find that video- and channel-level emotions independently influence audience emotions, providing evidence for both contagion and homophily effects. Random slope models suggest that contagion strength varies between YouTube channels for some emotions. However, neither dispositional channel-level emotions nor number of subscribers significantly moderate the strength of contagion effects. The present study highlights that contagion and homophily independently shape emotions in online social networks,” the authors said.
As you can see in the diagrams above, there appears to be an immediate (contagion) effect of watching a particular vlog and also a sustained (homophily) effect that leads to YouTuber emotions and viewers’ emotions mirroring each other.
“Our research is a reminder that the people we encounter online influence our everyday emotions — being exposed to happy (or angry) people can make us more happy (or angry) ourselves,” Rosenbusch concluded in a statement.
Follow the Money: Who Were the Highest-Paid YouTubers in 2018?
After reading about the latest research (Rosenbusch et al., 2018) on how emotional contagions can spread like wildfire via YouTube, I was curious to do a deeper dive into the current “birds of a feather flocking together” zeitgeist. Money talks. So, I asked myself: Which YouTubers are creating the biggest “homophily” ripple-effect based on how much income each vlogger generated as a cult-of-personality brand in 2018?
A quick Google search led to a recent Forbes article listing the “Highest-Paid YouTube Stars of 2018.” Based on these rankings, I’ve curated a top-ten list of vlogs from these YouTubers. If you have time, take a few minutes to watch some of these vloggers as a “homophily” guinea pig.
Does watching any of the vlogs below make you feel as if you’ve been exposed to a positive or negative emotional contagion, thus corroborating the latest findings by Rosenbusch and colleagues at Tilburg University?
Forbes List: Top-Ten Highest-Earning YouTube Stars 2018
10. Logan Paul Vlogs (8,688,873 subscribers/2018 Earnings: $14.5 million)
9. PewDiePie (78,487,516 subscribers/2018 Earnings: $15.5 million)
8. Jacksepticeye (21,040,954 subscribers/2018 Earnings: $16 million)
7. Vanoss Gaming (24,023,654 subscribers/2018 Earnings: $17 million)
6. Markiplier (22,702,844 subscribers/2018 Earnings: $17.5 million)
5. Jeffree Star (11,722,503 subscribers/2018 Earnings: $18 million)
4. DanTDM (20,809,880 subscribers/2018 Earnings: $18.5 million)
3. Dude Perfect (37,594,502 subscribers/2018 Earnings: $20 million)
2. Jake Paul (17,624,706 subscribers/2018 Earnings: $21.5 million)
1. Ryan ToysReview (17,585,225 subscribers/2018 Earnings: $22 million)
Hannes Rosenbusch, Anthony Evans, and Marcel Zeelenberg. “Multilevel Emotion Transfer on Youtube: Disentangling the Effects of Emotional Contagion and Homophily on Video Audiences” Social Psychological and Personality Science (First published online: December 27, 2018) DOI: 10.1177/1948550618820309
Miller McPherson, Lynn Smith-Lovin, and James M. Cook. “Birds of a Feather: Homophily in Social Networks” Annual Review of Sociology (Volume publication date: August 2001) DOI: 10.1146/annurev.soc.27.1.415
Christopher Bergland is a world-class endurance athlete, coach, author, and political activist.
4 Things you should know about augmented reality and 3D holograms.
For those of us – from aging baby boomers to generation Z – struggling with the question of how to move into the future and hold on to what we love about the past, this brief article offers some insight into the newest technology that will very soon impact all aspects of life.
The arrival of augmented reality (AR) seems inevitable ever since the launch of Google Glass in 2013. Since then, developers have been racing to deliver wearable devices with powers originally classified as science fiction. These capabilities include augmenting reality with your own physical world, projecting high resolution 3D images, and manipulating those images with your hands.
1. What is Augmented Reality?
Augmented Reality (AR) is the integration of digital information with the user’s environment in real time. Think Pokemon Go and Snapchap. In contrast, Virtual Reality (VR) – which has been around for some time – creates a totally artificial environment.
2. AR will arrive soon.
Tech futurists Scoble and Israel (2017) predict that more people will be using head mounted displays than hand held devices (smart phones) by 2025.
If this transformation seems impossibly fast, keep in mind that two of the top “revolutionary” ideas of TechCrunch 2006 were the BlackBerry Pearl and the iPod Shuffle – two devices that are have been mostly forgotten in less than a decade. Computer scientists have a theory that predicts this change. The idea, introduced in 1965, that computer power doubles every two years at the same cost is known as Moore’s Law.
3. Is this a big deal?
According to Touchstone Research, AR is poised to swallow personal computing as we know it in the near future. By their count, over 40 AR headset and glasses products are already on the market or in the advanced stages of development. The race includes established companies like Microsoft, Google, and Apple, in addition to dozens of well-funded start-ups. Estimates of the total market size of the AR business by 2025 range from $80 and $162 billion. By comparison, the current global market size for television sets is roughly $80 billion.
4. Imagine the Possibilities!
AR promises to be a tool that provides a quantum leap in psychology education and clinical applications. Here are a few examples:
AR for the Deaf: HoloHear is a HoloLens application that translates speech into sign language. When deaf people run the app, they see an avatar using sign language as well as subtitles.
AR for Autism: AR systems are now being used to encourage autistic children toward more imaginative play. Autism Speaks has funded a project to teach autistic teens about social skills in job interview settings and meetings with new people.
Other applications include an AR treatment for phantom limb pain, overlays for surgeries, and post-stroke hand rehabilitation. The fact that the user can actually see his own hands and the real world can help in exposure therapies for several types of psychological problems, such as spider and cock-roach phobias.
We are only at the tip of the iceberg with this new technology.
Scoble, R. & Israel, S. (2017). The fourth transformation: How augmented reality and artificial intelligence change everything. Patrick Brewster Press.
Kevin Bennett, Ph.D., is a social-personality psychologist, Assistant Teaching Professor, and Director of the Personality and Human Performance Lab (PHPL) at The Pennsylvania State University, Beaver Campus.
People who play video games for more than 15 hours per week performed better in learning tasks and showed increased activity in brain areas associated with memory than non-gamers, a new study reports.
Neuropsychologists of the Ruhr-Universität Bochum let video gamers compete against non-gamers in a learning competition. During the test, the video gamers performed significantly better and showed an increased brain activity in the brain areas that are relevant for learning. Prof Dr Boris Suchan, Sabrina Schenk and Robert Lech report their findings in the journal Behavioural Brain Research.
The weather prediction task
The research team studied 17 volunteers who – according to their own statement – played action-based games on the computer or a console for more than 15 hours a week. The control group consisted of 17 volunteers who didn’t play video games on a regular basis. Both teams did the so-called weather prediction task, a well-established test to investigate the learning of probabilities. The researchers simultaneously recorded the brain activity of the participants via magnetic resonance imaging.
The participants were shown a combination of three cue cards with different symbols. They should estimate whether the card combination predicted sun or rain and got a feedback if their choice was right or wrong right away. The volunteers gradually learned, on the basis of the feedback, which card combination stands for which weather prediction. The combinations were thereby linked to higher or lower probabilities for sun and rain. After completing the task, the study participants filled out a questionnaire to sample their acquired knowledge about the cue card combinations.
Video gamers better with high uncertainties
The gamers were notably better in combining the cue cards with the weather predictions than the control group. They fared even better with cue card combinations that had a high uncertainty such as a combination that predicted 60 percent rain and 40 percent sunshine.
The analysis of the questionnaire revealed that the gamers had acquired more knowledge about the meaning of the card combinations than the control group. “Our study shows that gamers are better in analysing a situation quickly, to generate new knowledge and to categorise facts – especially in situations with high uncertainties,” says first author Sabrina Schenk.
This kind of learning is linked to an increased activity in the hippocampus, a brain region that plays a key role in learning and memory. “We think that playing video games trains certain brain regions like the hippocampus”, says Schenk. “That is not only important for young people, but also for older people; this is because changes in the hippocampus can lead to a decrease in memory performance. Maybe we can treat that with video games in the future.”
Funding: Funded by German Research Foundation.
Source: Boris Suchan – RUB
Image Source: NeuroscienceNews.com image is in the public domain.
Original Research: Abstract for “Games people play: How video games improve probabilistic learning” by Schenk S, Lech RK, and Suchan B in Behavioral Brain Research. Published online August 24 2017 doi:10.1016/j.bbr.2017.08.027
Researchers report our visual attention pays most attention to parts of a scene that have meaning to us, not the parts that stick out.
Source: UC Davis.
Our visual attention is drawn to parts of a scene that have meaning, rather than to those that are salient or “stick out,” according to new research from the Center for Mind and Brain at the University of California, Davis. The findings, published Sept. 25 in the journal Nature Human Behavior, overturn the widely-held model of visual attention.
“A lot of people will have to rethink things,” said Professor John Henderson, who led the research. “The saliency hypothesis really is the dominant view.”
Our eyes we perceive a wide field of view in front of us, but we only focus our attention on a small part of this field. How do we decide where to direct our attention, without thinking about it?
The dominant theory in attention studies is “visual salience,” Henderson said. Salience means things that “stick out” from the background, like colorful berries on a background of leaves or a brightly lit object in a room.
Saliency is relatively easy to measure. You can map the amount of saliency in different areas of a picture by measuring relative contrast or brightness, for example.
Henderson called this the “magpie theory” our attention is drawn to bright and shiny objects.
“It becomes obvious, though, that it can’t be right,” he said, otherwise we would constantly be distracted.
Making a Map of Meaning
Henderson and postdoctoral researcher Taylor Hayes set out to test whether attention is guided instead by how “meaningful” we find an area within our view. They first had to construct “meaning maps” of test scenes, where different parts of the scene had different levels of meaning to an observer.
To make their meaning map, Henderson and Hayes took images of scenes, broke them up into overlapping circular tiles, and submitted the individual tiles to the online crowdsourcing service Mechanical Turk, asking users to rate the tiles for meaning.
By tallying the votes of Mechanical Turk users they were able to assign levels of meaning to different areas of an image and create a meaning map comparable to a saliency map of the same scene.
Next, they tracked the eye movements of volunteers as they looked at the scene. Those eyetracks gave them a map of what parts of the image attracted the most attention. This “attention map” was closer to the meaning map than the salience map, Henderson said.
In Search of Meaning
Henderson and Hayes don’t yet have firm data on what makes part of a scene meaningful, although they have some ideas. For example, a cluttered table or shelf attracted more attention than a highly salient splash of sunlight on a wall. With further work, they hope to develop a “taxonomy of meaning,” Henderson said.
Although the research is aimed at a fundamental understanding of how visual attention works, there could be some near-term applications, Henderson said, for example in developing automated visual systems that allow computers to scan security footage or to automatically identify or caption images online.
Funding: The work was supported by the National Science Foundation.
Source: Andy Fell – UC Davis
Image Source: NeuroscienceNews.com image is credited to John Henderson and Taylor Hayes, UC Davis.
Original Research: Abstract for “Meaning-based guidance of attention in scenes as revealed by meaning maps” by John M. Henderson & Taylor R. Hayes in Nature Human Nature. Published online September 25 2017 doi:10.1038/s41562-017-0208-0
Information on social media can be misleading because of biases in three places – the brain, society and algorithms. Scholars are developing ways to identify and display the effects of these biases.
Social media are among the primary sources of news in the U.S. and across the world. Yet users are exposed to content of questionable accuracy, including conspiracy theories, clickbait, hyperpartisan content, pseudo science and even fabricated “fake news” reports.
It’s not surprising that there’s so much disinformation published: Spam and online fraud are lucrative for criminals, and government and political propaganda yield both partisan and financial benefits. But the fact that low-credibility content spreads so quickly and easily suggests that people and the algorithms behind social media platforms are vulnerable to manipulation.
Our research has identified three types of bias that make the social media ecosystem vulnerable to both intentional and accidental misinformation. That is why our Observatory on Social Media at Indiana University is building tools to help people become aware of these biases and protect themselves from outside influences designed to exploit them.
Bias in the brain
Cognitive biases originate in the way the brain processes the information that every person encounters every day. The brain can deal with only a finite amount of information, and too many incoming stimuli can cause information overload. That in itself has serious implications for the quality of information on social media. We have found that steep competition for users’ limited attention means that some ideas go viral despite their low quality – even when people prefer to share high-quality content.
One cognitive shortcut happens when a person is deciding whether to share a story that appears on their social media feed. People are very affected by the emotional connotations of a headline, even though that’s not a good indicator of an article’s accuracy. Much more important is who wrote the piece.
To counter this bias, and help people pay more attention to the source of a claim before sharing it, we developed Fakey, a mobile news literacy game (free on Android and iOS) simulating a typical social media news feed, with a mix of news articles from mainstream and low-credibility sources. Players get more points for sharing news from reliable sources and flagging suspicious content for fact-checking. In the process, they learn to recognize signals of source credibility, such as hyperpartisan claims and emotionally charged headlines.
Bias in society
Another source of bias comes from society. When people connect directly with their peers, the social biases that guide their selection of friends come to influence the information they see.
In fact, in our research we have found that it is possible to determine the political leanings of a Twitter user by simply looking at the partisan preferences of their friends. Our analysis of the structure of these partisan communication networks found social networks are particularly efficient at disseminating information – accurate or not – when they are closely tied together and disconnected from other parts of society.
The tendency to evaluate information more favorably if it comes from within their own social circles creates “echo chambers” that are ripe for manipulation, either consciously or unintentionally. This helps explain why so many online conversations devolve into “us versus them” confrontations.
To study how the structure of online social networks makes users vulnerable to disinformation, we built Hoaxy, a system that tracks and visualizes the spread of content from low-credibility sources, and how it competes with fact-checking content. Our analysis of the data collected by Hoaxy during the 2016 U.S. presidential elections shows that Twitter accounts that shared misinformation were almost completely cut offfrom the corrections made by the fact-checkers.
When we drilled down on the misinformation-spreading accounts, we found a very dense core group of accounts retweeting each other almost exclusively – including several bots. The only times that fact-checking organizations were ever quoted or mentioned by the users in the misinformed group were when questioning their legitimacy or claiming the opposite of what they wrote.
Bias in the machine
The third group of biases arises directly from the algorithms used to determine what people see online. Both social media platforms and search engines employ them. These personalization technologies are designed to select only the most engaging and relevant content for each individual user. But in doing so, it may end up reinforcing the cognitive and social biases of users, thus making them even more vulnerable to manipulation.
For instance, the detailed advertising tools built into many social media platforms let disinformation campaigners exploit confirmation bias by tailoring messages to people who are already inclined to believe them.
Also, if a user often clicks on Facebook links from a particular news source, Facebook will tend to show that person more of that site’s content. This so-called “filter bubble” effect may isolate people from diverse perspectives, strengthening confirmation bias.
Our own research shows that social media platforms expose users to a less diverse set of sources than do non-social media sites like Wikipedia. Because this is at the level of a whole platform, not of a single user, we call this the homogeneity bias.
Another important ingredient of social media is information that is trending on the platform, according to what is getting the most clicks. We call this popularity bias, because we have found that an algorithm designed to promote popular content may negatively affect the overall quality of information on the platform. This also feeds into existing cognitive bias, reinforcing what appears to be popular irrespective of its quality.
All these algorithmic biases can be manipulated by social bots, computer programs that interact with humans through social media accounts. Most social bots, like Twitter’s Big Ben, are harmless. However, some conceal their real nature and are used for malicious intents, such as boosting disinformation or falsely creating the appearance of a grassroots movement, also called “astroturfing.” We found evidence of this type of manipulation in the run-up to the 2010 U.S. midterm election.
To study these manipulation strategies, we developed a tool to detect social bots called Botometer. Botometer uses machine learning to detect bot accounts, by inspecting thousands of different features of Twitter accounts, like the times of its posts, how often it tweets, and the accounts it follows and retweets. It is not perfect, but it has revealed that as many as 15 percent of Twitter accounts show signs of being bots.
Using Botometer in conjunction with Hoaxy, we analyzed the core of the misinformation network during the 2016 U.S. presidential campaign. We found many bots exploiting both the cognitive, confirmation and popularity biases of their victims and Twitter’s algorithmic biases.
These bots are able to construct filter bubbles around vulnerable users, feeding them false claims and misinformation. First, they can attract the attention of human users who support a particular candidate by tweeting that candidate’s hashtags or by mentioning and retweeting the person. Then the bots can amplify false claims smearing opponents by retweeting articles from low-credibility sources that match certain keywords. This activity also makes the algorithm highlight for other users false stories that are being shared widely.
Understanding complex vulnerabilities
Even as our research, and others’, shows how individuals, institutions and even entire societies can be manipulated on social media, there are many questions left to answer. It’s especially important to discover how these different biases interact with each other, potentially creating more complex vulnerabilities.
Tools like ours offer internet users more information about disinformation, and therefore some degree of protection from its harms. The solutions will not likely be only technological, though there will probably be some technical aspects to them. But they must take into account the cognitive and social aspects of the problem.