The Interplay of Knowledge Overestimation, Social Media Use, and Populist Ideas: Cross-Sectional and Experimental Evidence From Germany and Taiwan - Niels G. Mede, Adrian Rauchfleisch, Julia Metag, Mike S. Schäfer, 2024

The limits of social media as a source of political information during routine and crisis times across 17 countries

Recent studies on political knowledge suggest people learn little about political events and societal issues from social media. Potentially, social media are a more effective source of information ...

Does news help us become knowledgeable or think we are knowledgeable? Examining a linkage of traditional and social media use with political knowledge

This study examines traditional and social media news use in relation to political knowledge from the perspective of the Dunning-Kruger effect. Data from a two-wave panel survey show that social me...

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...

Illusion of knowledge through Facebook news? Effects of snack news in a news feed on perceived knowledge, attitude strength, and willingness for discussions

Research indicates that using social network sites as a source for news increases perceived knowledge even if, objectively, people fail to acquire knowledge. This might result from the frequent repetition of topics in news posts caused by multiple news outlets posting about the same news topics and the algorithm that favors similar postings. These repeated encounters can have a positive effect on the perception of knowing more, even if actual learning hardly occurs. An experiment (N = 810, representative of German Internet users) tested these assumptions. Participants were assigned to one of four groups and received a news feed with no information, few news posts, many news posts, or a full-length news article. Results indicate that many news posts increased perceived knowledge that is not paralleled by a gain in factual knowledge. Perceived knowledge mediates effects of reading many news posts on more extreme attitudes and the willingness for discussions. Even if participants who read the news article gained factual knowledge, they did not feel more knowledgeable than participants who were exposed to a news feed containing news posts. The results emphasize the meaning of engaging with full news articles, both for learning facts and for more accurate knowledge assessments.

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.

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.

Shifts in U.S. Social Media Use, 2020–2024: Decline, Fragmentation, and Enduring Polarization

Using nationally representative data from the 2020 and 2024 American National Election Studies (ANES), this paper describes how U.S. social media use has shifted across platforms, demographics, and politics. Overall platform reach declined, driven by growth in the share of Americans — especially the youngest and oldest cohorts — who report using no social media. Visiting and posting activity on Twitter/X and Facebook have fallen by nearly 50% since 2020, with the decline on Twitter/X driven primarily by reduced participation among Democratic users. While Facebook, YouTube, and Twitter/X lost ground, TikTok and Reddit grew modestly, consistent with a more fragmented digital public sphere. Platform audiences aged and became slightly more educated and racially diverse. Politically, most platforms shifted toward Republican users while remaining, on balance, Democratic-leaning. Twitter/X experienced the largest change: among posters, the partisan balance swung over 70 percentage points from Democrats to Republicans. Across platforms, political posting remains closely tied to affective polarization, as the most partisan respondents are also the most active. As casual users disengage while polarized partisans remain vocal, online discourse becomes narrower and more ideologically extreme.

Do Science <i>Kardashians</i> Get Citation Premium? Self‐Fulfilling Effects of Social Media on Scientific Impact

ABSTRACT We analyze whether the visibility of scientists on social media affects the number of academic citations. We use the global COVID‐19 pandemic as a quasinatural experiment that exogenously increased public attention and the demand for expertise. Using publications on COVID‐related topics by social media stars and their coauthors prior to the outbreak of the pandemic, we find that social media stars' pre‐COVID‐era papers received about – more citations annually per paper after 2019. Quantitatively comparable results are obtained when we use scientists' Kardashian index (K‐index) as a benchmark for stardom, however we find no significant effects when using the intensive margin of scientists' K‐indexes. We provide a brief discussion of policy implications in light of these findings.

Do people learn about politics on social media? A meta-analysis of 76 studies

Abstract. Citizens turn increasingly to social media to get their political information. However, it is currently unclear whether using these platforms act

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.

Consequences of Information Feed Integration on User Engagement and Contribution: A Natural Experiment in an Online Knowledge-Sharing Community

Many online communities that rely on effortful, voluntary content contributions offer additional content curation tools to facilitate social interactions and encourage user contributions. Any platform that offers two or more heterogeneous content types (e.g., expert knowledge and social posts) faces a choice about the presentation format: whether to display the content types separately or in an integrated information feed. We leverage a natural experiment on Zhihu, a Q&A platform that offers a social-interaction-oriented functionality called Ideas. Zhihu initially presented answers (expert knowledge content) and ideas (social posts) in two different information feeds, but the platform integrated ideas into the same information feed as answers in June 2019. We find that information feed integration significantly decreased user engagement with and contribution of both ideas and answers. We hypothesize that users decreased their engagement because the juxtaposition of incongruous types of content increased mindset switching and cognitive strain. This hypothesis is supported by an additional laboratory experiment. We also present evidence showing that contributions decreased both because of the decrease in engagement (weaker social recognition incentives) and because integration heightened concerns that posting ideas would dilute the contributor’s professional image. Our findings have important theoretical and practical implications for any platform that hosts heterogeneous content. History: Xiaoquan (Michael) Zhang served as the senior editor and Yili (Kevin) Hong served as associate editor for this article. Funding: Z. Cao acknowledges this research was funded by National Natural Science Foundation of China [Grants 72201238, 72192823]. G. Li acknowledges this research was funded by National Natural Science Foundation of China [Grant 72102047] and Shanghai Pujiang Program [Grant 21PJC006]. Supplemental Material: The e-companion is available at https://doi.org/10.1287/isre.2022.0043.

Estimating the association between Facebook adoption and well-being in 72 countries

Social media's potential effects on well-being have received considerable research interest, but much of past work is hampered by an exclusive focus on demographics in the Global North and inaccurate self-reports of social media engagement. We describe associations linking 72 countries' Facebook adoption to the well-being of 946 798 individuals from 2008 to 2019. We found no evidence suggesting that the global penetration of social media is associated with widespread psychological harm: Facebook adoption predicted life satisfaction and positive experiences positively, and negative experiences negatively, both between countries and within countries over time. Nevertheless, the observed associations were small and did not reach a conventional 97.5% one-sided credibility threshold in all cases. Facebook adoption predicted aspects of well-being more positively for younger individuals, but country-specific results were mixed. To move beyond studying aggregates and to better understand social media's roles in people's lives, and their potential causal effects, we need more transparent collaborative research between independent scientists and the technology industry.

Redesigning algorithms to intervene on social norm misperceptions during a national election

For the first time in history, civic discourse commonly occurs in digital environments in which algorithms influence exposure to social information1,2. It is increasingly important to understand whether and how these algorithms affect political discourse3–5. Here we built custom feed-ranking algorithms with full control over their features, and randomly assigned 2,000 participants to use them for 8 weeks (before and after the 2024 US presidential election). We tested whether an engagement-based algorithm (used on major social media platforms6,7) amplifies intergroup, moralized and emotional (IME) information in ways that skew perceptions of social norms around political dialogue5,8, and whether it increased engagement with IME content and perceptions of partisan animosity (compared with a reverse-chronological feed9,10). We also developed and tested a ‘diversified extremity’ algorithm to reduce the influence of extreme users11–13 to improve the accuracy of social norm perception14–16 and reduce perceptions of partisan animosity. We found that engagement-based feeds amplified IME and toxic content relative to reverse-chronological feeds, with the largest increases in moral outrage and political content. Engagement-based feeds also reduced prescriptive norm perception accuracy (albeit in an unexpected direction) and increased perceived partisan animosity. However, they did not significantly alter users’ own engagement behaviours. The diversified extremity algorithm reduced IME and toxic content exposure, improved prescriptive norm accuracy, yet maintained comparable platform enjoyment—suggesting that reducing the influence of extreme users can curb algorithmic distortions without diminishing user experience.

The political effects of X’s feed algorithm

Feed algorithms are widely suspected to influence political attitudes. However, previous evidence from switching off the algorithm on Meta platforms found no political effects1. Here we present results from a 2023 field experiment on Elon Musk’s platform X shedding light on this puzzle. We assigned active US-based users randomly to either an algorithmic or a chronological feed for 7 weeks, measuring political attitudes and online behaviour. Switching from a chronological to an algorithmic feed increased engagement and shifted political opinion towards more conservative positions, particularly regarding policy priorities, perceptions of criminal investigations into Donald Trump and views on the war in Ukraine. In contrast, switching from the algorithmic to the chronological feed had no comparable effects. Neither switching the algorithm on nor switching it off significantly affected affective polarization or self-reported partisanship. To investigate the mechanism, we analysed users’ feed content and behaviour. We found that the algorithm promotes conservative content and demotes posts by traditional media. Exposure to algorithmic content leads users to follow conservative political activist accounts, which they continue to follow even after switching off the algorithm, helping explain the asymmetry in effects. These results suggest that initial exposure to X’s algorithm has persistent effects on users’ current political attitudes and account-following behaviour, even in the absence of a detectable effect on partisanship.