There is widespread concern about misinformation circulating on social media. In particular, many argue that the context of social media itself may make people susceptible to the influence of false claims. Here, we test that claim by asking whether simply considering sharing news on social media reduces the extent to which people discriminate truth from falsehood when judging accuracy. In a large online experiment examining coronavirus disease 2019 (COVID-19) and political news ( N = 3157 Americans), we find support for this possibility. When judging the accuracy of headlines, participants were worse at discerning truth from falsehood if they both evaluated accuracy and indicated their sharing intentions, compared to just evaluating accuracy. These results suggest that people may be particularly vulnerable to believing false claims on social media, given that sharing is a core element of what makes social media “social.” , Simply considering whether to share news on social media reduces the extent to which people discriminate truth from falsehood.
Sharing of misinformation is habitual, not just lazy or biased
Why do people share misinformation on social media? In this research ( N = 2,476), we show that the structure of online sharing built into social platforms is more important than individual deficits in critical reasoning and partisan bias—commonly cited drivers of misinformation. Due to the reward-based learning systems on social media, users form habits of sharing information that attracts others' attention. Once habits form, information sharing is automatically activated by cues on the platform without users considering response outcomes such as spreading misinformation. As a result of user habits, 30 to 40% of the false news shared in our research was due to the 15% most habitual news sharers. Suggesting that sharing of false news is part of a broader response pattern established by social media platforms, habitual users also shared information that challenged their own political beliefs. Finally, we show that sharing of false news is not an inevitable consequence of user habits: Social media sites could be restructured to build habits to share accurate information.
Sharing of misinformation is habitual, not just lazy or biased
Why do people share misinformation on social media? In this research ( N = 2,476), we show that the structure of online sharing built into social platforms is more important than individual deficits in critical reasoning and partisan bias—commonly cited drivers of misinformation. Due to the reward-based learning systems on social media, users form habits of sharing information that attracts others' attention. Once habits form, information sharing is automatically activated by cues on the platform without users considering response outcomes such as spreading misinformation. As a result of user habits, 30 to 40% of the false news shared in our research was due to the 15% most habitual news sharers. Suggesting that sharing of false news is part of a broader response pattern established by social media platforms, habitual users also shared information that challenged their own political beliefs. Finally, we show that sharing of false news is not an inevitable consequence of user habits: Social media sites could be restructured to build habits to share accurate information.
Sharing without clicking on news in social media
Social media have enabled laypersons to disseminate, at scale, links to news and public affairs information. Many individuals share such links without first reading the linked information. Here we analysed over 35 million public Facebook posts with uniform resource locators shared between 2017 and 2020, and discovered that such ‘shares without clicks’ (SwoCs) constitute around 75% of forwarded links. Extreme and user-aligned political content received more SwoCs, with partisans engaging in it more than politically neutral users. In addition, analyses with 2,969 false uniform resource locators revealed higher shares and, hence, SwoCs by conservatives (76.94%) than liberals (14.25%), probably because, in our dataset, the vast majority (76–82%) of them originated from conservative news domains. Findings suggest that the virality of political content on social media (including misinformation) is driven by superficial processing of headlines and blurbs rather than systematic processing of core content, which has design implications for promoting deliberate discourse in the online public sphere.
Following news on social media boosts knowledge, belief accuracy and trust
Many worry that news on social media leaves people uninformed or even misinformed. Here we conducted a preregistered two-wave online field experiment in France and Germany (N = 3,395) to estimate the effect of following the news on Instagram and WhatsApp. Participants were asked to follow two accounts for 2 weeks and activate the notifications. In the treatment condition, the accounts were those of news organizations, while in the control condition they covered cooking, cinema or art. The treatment enhanced current affairs knowledge, participants’ ability to discern true from false news stories and awareness of true news stories, as well as trust in the news. The treatment had no significant effects on feelings of being informed, political efficacy, affective polarization and interest in news or politics. These results suggest that, while some forms of social media use are harmful, others are beneficial and can be leveraged to foster a well-informed society.
The Role of Media in Political Polarization| Inoculation Can Reduce the Perceived Reliability of Polarizing Social Media Content
Little research is available on psychological interventions that counter susceptibility to polarizing online content. We conducted 3 studies (n1 = 472, n2 = 193, n3 = 772) to evaluate whether psychological resistance against polarizing social media content can be conferred, using the Bad News game, a “technique-based inoculation” intervention that simulates a social media feed. We investigate (1) whether technique-based inoculation can reduce susceptibility to content designed to fuel intergroup polarization; (2) whether technique-based inoculation can offer cross-protection against misinformation techniques that people were not inoculated against; and (3) whether political ideology plays a role in how people engage with anti-misinformation interventions. In Studies 1 and 3 (but not Study 2), we found that technique-based inoculation significantly reduces the perceived reliability of polarizing content and offers partial cross-protection against untreated misinformation techniques. We found no effect for attitudinal certainty and news-sharing intentions. Finally, we report preliminary evidence that people may choose to engage with politically congruent news topics within the intervention.
Facts and Myths about Misperceptions
Misperceptions threaten to warp mass opinion and public policy on controversial issues in politics, science, and health. What explains the prevalence and persistence of these false and unsupported beliefs, which seem to be genuinely held by many people? Though limits on cognitive resources and attention play an important role, many of the most destructive misperceptions arise in domains where individuals have weak incentives to hold accurate beliefs and strong directional motivations to endorse beliefs that are consistent with a group identity such as partisanship. These tendencies are often exploited by elites who frequently create and amplify misperceptions to influence elections and public policy. Though evidence is lacking for claims of a “post-truth” era, changes in the speed with which false information travels and the extent to which it can find receptive audiences require new approaches to counter misinformation. Reducing the propagation and influence of false claims will require further efforts to inoculate people in advance of exposure (for example, media literacy), debunk false claims that are already salient or widespread (for example, fact-checking), reduce the prevalence of low-quality information (for example, changing social media algorithms), and discourage elites from promoting false information (for example, strengthening reputational sanctions).
How Social Media Rewards Misinformation
A majority of false stories are spread by a small number of frequent users, suggests a new study co-authored by Yale SOM’s Gizem Ceylan. But they can be taught to change their ways.
Evaluating Twitter’s algorithmic amplification of low-credibility content: an observational study
Artificial intelligence (AI)-powered recommender systems play a crucial role in determining the content that users are exposed to on social media platforms. However, the behavioural patterns of these systems are often opaque, complicating the evaluation of their impact on the dissemination and consumption of disinformation and misinformation. To begin addressing this evidence gap, this study presents a measurement approach that uses observed digital traces to infer the status of algorithmic amplification of low-credibility content on Twitter over a 14-day period in January 2023. Using an original dataset of ≈ 2.7 million posts on COVID-19 and climate change published on the platform, this study identifies tweets sharing information from low-credibility domains, and uses a bootstrapping model with two stratifications, a tweet’s engagement level and a user’s followers level, to compare any differences in impressions generated between low-credibility and high-credibility samples. Additional stratification variables of toxicity, political bias, and verified status are also examined. This analysis provides valuable observational evidence on whether the Twitter algorithm favours the visibility of low-credibility content, with results indicating that, on aggregate, tweets containing low-credibility URL domains perform better than tweets that do not across both datasets. However, this effect is largely attributable to a difference in high-engagement, high-followers tweets, which are very impactful in terms of impressions generation, and are more likely receive amplified visibility when containing low-credibility content. Furthermore, high toxicity tweets and those with right-leaning bias see heightened amplification, as do low-credibility tweets from verified accounts. Ultimately, this suggests that Twitter’s recommender system may have facilitated the diffusion of false content by amplifying the visibility of low-credibility content with high-engagement generated by very influential users.
Evaluating Twitter’s algorithmic amplification of low-credibility content: an observational study
Artificial intelligence (AI)-powered recommender systems play a crucial role in determining the content that users are exposed to on social media platforms. However, the behavioural patterns of these systems are often opaque, complicating the evaluation of their impact on the dissemination and consumption of disinformation and misinformation. To begin addressing this evidence gap, this study presents a measurement approach that uses observed digital traces to infer the status of algorithmic amplification of low-credibility content on Twitter over a 14-day period in January 2023. Using an original dataset of ≈ 2.7 million posts on COVID-19 and climate change published on the platform, this study identifies tweets sharing information from low-credibility domains, and uses a bootstrapping model with two stratifications, a tweet’s engagement level and a user’s followers level, to compare any differences in impressions generated between low-credibility and high-credibility samples. Additional stratification variables of toxicity, political bias, and verified status are also examined. This analysis provides valuable observational evidence on whether the Twitter algorithm favours the visibility of low-credibility content, with results indicating that, on aggregate, tweets containing low-credibility URL domains perform better than tweets that do not across both datasets. However, this effect is largely attributable to a difference in high-engagement, high-followers tweets, which are very impactful in terms of impressions generation, and are more likely receive amplified visibility when containing low-credibility content. Furthermore, high toxicity tweets and those with right-leaning bias see heightened amplification, as do low-credibility tweets from verified accounts. Ultimately, this suggests that Twitter’s recommender system may have facilitated the diffusion of false content by amplifying the visibility of low-credibility content with high-engagement generated by very influential users.
The Liar’s Dividend: Can Politicians Claim Misinformation to Evade Accountability?
This study addresses the phenomenon of misinformation about misinformation, or politicians "crying wolf"' over fake news. Strategic and false claims that stories are fake news or deepfakes may benefit politicians by helping them maintain support after a scandal. We posit that this benefit, known as the "liar's dividend," may be achieved through two politician strategies: by invoking informational uncertainty or by encouraging oppositional rallying of core supporters. We administer five survey experiments to over 15,000 American adults detailing hypothetical politician responses to stories describing real politician scandals. We find that claims of misinformation representing both strategies raise politician support across partisan subgroups. These strategies are effective against text-based reports of scandals, but are largely ineffective against video evidence and do not reduce general trust in media. Finally, these false claims produce greater dividends for politicians than alternative responses to scandal, such as remaining silent or apologizing.
Trust: Foundation of Human Knowledge yet online Platforms won't allow | David Karger | TEDxMIT Salon
Fakebook: Using Data Science to Spot Fake News
The echo chamber effect on social media
Significance We explore the key differences between the main social media platforms and how they are likely to influence information spreading and the formation of echo chambers. To assess the different dynamics, we perform a comparative analysis on more than 100 million pieces of content concerning controversial topics (e.g., gun control, vaccination, abortion) from Gab, Facebook, Reddit, and Twitter. The analysis focuses on two main dimensions: 1) homophily in the interaction networks and 2) bias in the information diffusion toward like-minded peers. Our results show that the aggregation in homophilic clusters of users dominates online dynamics. However, a direct comparison of news consumption on Facebook and Reddit shows higher segregation on Facebook. , Social media may limit the exposure to diverse perspectives and favor the formation of groups of like-minded users framing and reinforcing a shared narrative, that is, echo chambers. However, the interaction paradigms among users and feed algorithms greatly vary across social media platforms. This paper explores the key differences between the main social media platforms and how they are likely to influence information spreading and echo chambers’ formation. We perform a comparative analysis of more than 100 million pieces of content concerning several controversial topics (e.g., gun control, vaccination, abortion) from Gab, Facebook, Reddit, and Twitter. We quantify echo chambers over social media by two main ingredients: 1) homophily in the interaction networks and 2) bias in the information diffusion toward like-minded peers. Our results show that the aggregation of users in homophilic clusters dominate online interactions on Facebook and Twitter. We conclude the paper by directly comparing news consumption on Facebook and Reddit, finding higher segregation on Facebook.
The spreading of misinformation online
Significance The wide availability of user-provided content in online social media facilitates the aggregation of people around common interests, worldviews, and narratives. However, the World Wide Web is a fruitful environment for the massive diffusion of unverified rumors. In this work, using a massive quantitative analysis of Facebook, we show that information related to distinct narratives––conspiracy theories and scientific news––generates homogeneous and polarized communities (i.e., echo chambers) having similar information consumption patterns. Then, we derive a data-driven percolation model of rumor spreading that demonstrates that homogeneity and polarization are the main determinants for predicting cascades’ size. , The wide availability of user-provided content in online social media facilitates the aggregation of people around common interests, worldviews, and narratives. However, the World Wide Web (WWW) also allows for the rapid dissemination of unsubstantiated rumors and conspiracy theories that often elicit rapid, large, but naive social responses such as the recent case of Jade Helm 15––where a simple military exercise turned out to be perceived as the beginning of a new civil war in the United States. In this work, we address the determinants governing misinformation spreading through a thorough quantitative analysis. In particular, we focus on how Facebook users consume information related to two distinct narratives: scientific and conspiracy news. We find that, although consumers of scientific and conspiracy stories present similar consumption patterns with respect to content, cascade dynamics differ. Selective exposure to content is the primary driver of content diffusion and generates the formation of homogeneous clusters, i.e., “echo chambers.” Indeed, homogeneity appears to be the primary driver for the diffusion of contents and each echo chamber has its own cascade dynamics. Finally, we introduce a data-driven percolation model mimicking rumor spreading and we show that homogeneity and polarization are the main determinants for predicting cascades’ size.
Why Don’t We Learn from Social Media? Studying Effects of and Mechanisms behind Social Media News Use on General Surveillance Political Knowledge
Does exposure to news affect what people know about politics? This old question attracted new scholarly interest as the political information environment is changing rapidly. In particular, since c...
