Out-group animosity drives engagement on social media

The effects of Facebook and Instagram on the 2020 election: A deactivation experiment

We study the effect of Facebook and Instagram access on political beliefs, attitudes, and behavior by randomizing a subset of 19,857 Facebook users and 15,585 Instagram users to deactivate their accounts for 6 wk before the 2020 U.S. election. We report four key findings. First, both Facebook and Instagram deactivation reduced an index of political participation (driven mainly by reduced participation online). Second, Facebook deactivation had no significant effect on an index of knowledge, but secondary analyses suggest that it reduced knowledge of general news while possibly also decreasing belief in misinformation circulating online. Third, Facebook deactivation may have reduced self-reported net votes for Trump, though this effect does not meet our preregistered significance threshold. Finally, the effects of both Facebook and Instagram deactivation on affective and issue polarization, perceived legitimacy of the election, candidate favorability, and voter turnout were all precisely estimated and close to zero.

Preregistered Replication and Extension of "Moral Hypocrisy: Social Groups and the Flexibility of Virtue"

The tendency for people to consider themselves morally good while behaving selfishly is known as moral hypocrisy. Influential work by Valdesolo and DeSteno (2007) found evidence for intergroup moral hypocrisy such that people were more forgiving of transgressions when they were committed by an in-group member than an out-group member. We conducted two experiments to examine moral hypocrisy and group membership in an online paradigm with Prolific workers from the United States: a direct replication of the original work with minimal groups (N = 610; nationally representative) and a conceptual replication with political groups (N = 606; 50% Democrats and 50% Republicans). Although the results did not replicate the original findings, we observed evidence of in-group favoritism in minimal groups and out-group derogation in political groups. The current research finds mixed evidence of intergroup moral hypocrisy and has implications for understanding the contextual dependencies of intergroup bias and partisanship.

Temporal dynamics of coordinated online behavior: Stability, archetypes, and influence

Large-scale online campaigns, malicious or otherwise, require a significant degree of coordination among participants, which sparked interest in the study of coordinated online behavior. State-of-the-art methods for detecting coordinated behavior perform static analyses, disregarding the temporal dynamics of coordination. Here, we carry out a dynamic analysis of coordinated behavior. To reach our goal, we build a multiplex temporal network and we perform dynamic community detection to identify groups of users that exhibited coordinated behaviors in time. We find that i) coordinated communities (CCs) feature variable degrees of temporal instability; ii) dynamic analyses are needed to account for such instability, and results of static analyses can be unreliable and scarcely representative of unstable communities; iii) some users exhibit distinct archetypal behaviors that have important practical implications; iv) content and network characteristics contribute to explaining why users leave and join CCs. Our results demonstrate the advantages of dynamic analyses and open up new directions of research on the unfolding of online debates, on the strategies of CCs, and on the patterns of online influence.

Changing the incentive structure of social media may reduce online proxy failure and proliferation of negativity

Social media takes advantage of people's predisposition to attend to threatening stimuli by promoting content in algorithms that capture attention. However, this content is often not what people expressly state they would like to see. We propose that social media companies should weigh users' expressed preferences more heavily in algorithms. We propose modest changes to user interfaces that could reduce the abundance of threatening content in the online environment.

AI can help people feel heard, but an AI label diminishes this impact

People want to "feel heard" to perceive that they are understood, validated, and valued. Can AI serve the deeply human function of making others feel heard? Our research addresses two fundamental issues: Can AI generate responses that make human recipients feel heard, and how do human recipients react when they believe the response comes from AI? We conducted an experiment and a follow-up study to disentangle the effects of actual source of a message and the presumed source. We found that AI-generated messages made recipients feel more heard than human-generated messages and that AI was better at detecting emotions. However, recipients felt less heard when they realized that a message came from AI (vs. human). Finally, in a follow-up study where the responses were rated by third-party raters, we found that compared with humans, AI demonstrated superior discipline in offering emotional support, a crucial element in making individuals feel heard, while avoiding excessive practical suggestions, which may be less effective in achieving this goal. Our research underscores the potential and limitations of AI in meeting human psychological needs. These findings suggest that while AI demonstrates enhanced capabilities to provide emotional support, the devaluation of AI responses poses a key challenge for effectively leveraging AI's capabilities.

Human-algorithm interactions help explain the spread of misinformation

Human attention biases toward moral and emotional information are as prevalent online as they are offline. When these biases interact with content algorithms that curate social media users' news feeds to maximize attentional capture, moral and emotional information are privileged in the online information ecosystem. We review evidence for these human-algorithm interactions and argue that misinformation exploits this process to spread online. This framework suggests that interventions aimed at combating misinformation require a dual-pronged approach that combines person-centered and design-centered interventions to be most effective. We suggest several avenues for research in the psychological study of misinformation sharing under a framework of human-algorithm interaction.

The 2024 Taiwanese Presidential Election on social media: Identity, policy, and affective virality

The 2024 Taiwanese Presidential Election is not just a critical geopolitical event, it also engages with themes of alternative candidacy, foreign policy, and affective polarization. At one point, a four-candidate race had emerged in a traditionally bipartisan election, with alternative candidates disrupting the dichotomy of Chinese vs. Taiwanese identity. Leveraging 911,510 posts and 101,600,047 engagements on social media, we analyze user discourse and engagement. First, we find traditional candidates derive more engagement on foreign policy and geopolitical issues, alternative candidates on domestic issues. Additionally, virality is generated by affective reasons, although in-group references generate more engagement than out-group references. Lastly, a puzzle is revealed where alternative candidates draw more homogeneous attention from national identity groups. Results suggest alternative candidacy can be generated by both positive and negative comparisons rooted in national identity.

Updating the identity-based model of belief: From false belief to the spread of misinformation

The spread of misinformation threatens democratic societies, hampering informed decision-making. Partisan identity biases perceptions of reality, promoting false beliefs. The Identity-based Model of Political Belief explains how social identity shapes information processing and contributes to misinformation. According to this model, social identity goals can override accuracy goals, leading to belief alignment with party members rather than facts. We propose an extended version of this model that incorporates the role of informational context in misinformation belief and sharing. Partisanship involves cognitive and motivational aspects that shape party members' beliefs and actions. This includes whether they seek further evidence, where they seek that evidence, and which sources they trust. Understanding the interplay between social identity and accuracy is crucial in addressing misinformation.

To meme or not to meme? Political social media posts and ideologically motivated aggression in job recommendations

This study tested the notion of ideological asymmetry, which proposes that conservatives are more prejudiced than liberals. It involved 682 self-identified conservative (n = 383) and liberal (n = 299) perceivers (MTurk workers; 54% female) who evaluated a target person's professional attributes, personal character, and job suitability based on the target's social media posts. The results did not support ideological asymmetry as both conservative and liberal participants negatively evaluated an ideologically opposite target. Interestingly, liberals showed three times more bias than conservatives. This study better supports a worldview conflict hypothesis, an alternative to ideological asymmetry, with both sides showing indirect aggression in an apolitical setting.

Social Media Posts About Carpal Tunnel Release: A Cross-Sectional Analysis of Patient and Surgeon Perspectives

BACKGROUND

Social media provides an increasingly popular, unfiltered source of perspectives on healthcare. The objective of this study is to characterize the landscape of social media posts regarding carpal tunnel release (CTR).

METHODS

Content was queried from Instagram between February 2, 2019 to August 12, 2021 using the hashtags #carpaltunnelrelease and #carpaltunnelsurgery. The 1500 most-liked posts were analyzed. Poster demographics including age, gender, region, and symptom qualities and post characteristics including type, number, timing relative to surgery, tone, and satisfaction were collected. Categorical variables were compared utilizing chi-squared test. Univariate and multivariate regression were performed.

RESULTS

The most popular post types included single photo (55.2%), multiple photos (18.8%), or single video (18.2%). Of all, 70.6% posts had fewer than 50 "likes." Patients accounted for 51.8% of posts, followed by surgeons (13.3%), other health care providers (11.7%), and physical therapists (8.8%). Women (66.7%) outnumbered men (33.3%). Fifty-five percent of posts were domestic. Posts mostly depicted postoperative care (85.6%). The most frequently mentioned symptoms were pain, burning, numbness, and tingling. Of all posts, 45.1% had a positive tone, 49.1% neutral, and 5.7% negative. Univariate analysis revealed that posters who were patients, underwent open CTR, and were female were more likely to post negative sentiments.

CONCLUSIONS

Most posts regarding CTR are from patients, are postoperative, and are positive or neutral. Although rare, negative posts were more likely to originate from posters who are patients, female, or underwent open CTR. With this information, surgeons will be better prepared to address patient concerns, set patient expectations, and enter the social media themselves.

Psychological inoculation strategies to fight climate disinformation across 12 countries

Decades after the scientific debate about the anthropogenic causes of climate change was settled, climate disinformation still challenges the scientific evidence in public discourse. Here we present a comprehensive theoretical framework of (anti)science belief formation and updating to account for the psychological factors that influence the acceptance or rejection of scientific messages. We experimentally investigated, across 12 countries (N = 6,816), the effectiveness of six inoculation strategies targeting these factors-scientific consensus, trust in scientists, transparent communication, moralization of climate action, accuracy and positive emotions-to fight real-world disinformation about climate science and mitigation actions. While exposure to disinformation had strong detrimental effects on participants' climate change beliefs (δ = -0.16), affect towards climate mitigation action (δ = -0.33), ability to detect disinformation (δ = -0.14) and pro-environmental behaviour (δ = -0.24), we found almost no evidence for protective effects of the inoculations (all δ < 0.20). We discuss the implications of these findings and propose ways forward to fight climate disinformation.

Social Media Posts About Distal Radius Fracture: A Cross-Sectional Analysis of Patient and Provider Perspectives

BACKGROUND

Social media offers a popular, unfiltered source of patient and provider perspectives on health care. This study investigated the characteristics of social media posts referencing distal radius fracture (DRF).

METHODS

Content was queried from Instagram from February 06, 2019, to December 08, 2021, using the hashtags "#distalradiusfracture" and "#wristfracture." The 1500 most-liked posts were analyzed. Poster demographics including age, gender, region, laterality, and treatment type and post characteristics including post type, number, content, timing relative to treatment, tone, and satisfaction were examined. Variables were compared using χ2 tests. Univariate, multivariate, and stepwise regression were performed.

RESULTS

The most popular post formats were single photo (44.5%), multiple photos (32.3%), and single video (13.9%). Patients (40.3%) were the most common poster followed by surgeons (33.4%). Men (48.7%) and women (51.3) were evenly represented. Of the total posts, 87.7% depicted the post-treatment phase of care; 54.8% of posts depicted operative management, while 26.6% depicted non-operative management; and 73.7% of posts were positive in tone, 18.7% neutral, and 7.6% negative. Univariate analysis demonstrated that posters who were patients, friends/family of patients, female, and posts with >100 "likes" were more likely to share negative tones. Multivariate and stepwise regression were consistent with the above.

CONCLUSION

Most posts regarding DRF originate from patients, are post-treatment, and are positive. Negative tone is associated with posts from patients, family/friends of patients, female posters, and posts with >100 likes. With this information, surgeons will be better prepared to address patient concerns, manage expectations, and actively participate in social media themselves.

Do fossil fuel firms reframe online climate and sustainability communication? A data-driven analysis

Identifying drivers of climate misinformation on social media is crucial to climate action. Misinformation comes in various forms; however, subtler strategies, such as emphasizing favorable interpretations of events or data or reframing conversations to fit preferred narratives, have received little attention. This data-driven paper examines online climate and sustainability communication behavior over 7 years (2014-2021) across three influential stakeholder groups consisting of eight fossil fuel firms (industry), 14 non-governmental organizations (NGOs), and eight inter-governmental organizations (IGOs). We examine historical Twitter interaction data (n = 668,826) using machine learning-driven joint-sentiment topic modeling and vector autoregression to measure online interactions and influences amongst these groups. We report three key findings. First, we find that the stakeholders in our sample are responsive to one another online, especially over topics in their respective areas of domain expertise. Second, the industry is more likely to respond to IGOs' and NGOs' online messaging changes, especially regarding environmental justice and climate action topics. The fossil fuel industry is more likely to discuss public relations, advertising, and corporate sustainability topics. Third, we find that climate change-driven extreme weather events and stock market performance do not significantly affect the patterns of communication among these firms and organizations. In conclusion, we provide a data-driven foundation for understanding the influence of powerful stakeholder groups on shaping the online climate and sustainability information ecosystem around climate change.

The event-driven nature of online political hostility: How offline political events make online interactions more hostile

Hostile interactions permeate political debates on social media, but what is driving the long-term developments in online political hostility? Prior research focuses on individual-level factors such as the dispositions of users or network-level factors such as echo chambers. Moving beyond these accounts, we develop and test an event-oriented explanation and demonstrate that over the course of the 2020 election year in the United States, all major shifts in political hostility on the social media platform Twitter were driven by external offline events. Importantly, these events were magnified by Twitter users within the most politically hostile and most ideologically homogeneous networks. Further contributing to the individual and network-oriented accounts, we show that divisive offline events mobilized individual users not already disposed for hostility and may have helped facilitate the formation of echo chambers. The dynamics of online interactions-including their level of hostility-seem crucially dependent on developments in the offline world.

The Costs of Polarizing a Pandemic: Antecedents, Consequences, and Lessons

Polarization has been rising in the United States of America for the past few decades and now poses a significant-and growing-public-health risk. One of the signature features of the American response to the COVID-19 pandemic has been the degree to which perceptions of risk and willingness to follow public-health recommendations have been politically polarized. Although COVID-19 has proven more lethal than any war or public-health crisis in American history, the deadly consequences of the pandemic were exacerbated by polarization. We review research detailing how every phase of the COVID-19 pandemic has been polarized, including judgments of risk, spatial distancing, mask wearing, and vaccination. We describe the role of political ideology, partisan identity, leadership, misinformation, and mass communication in this public-health crisis. We then assess the overall impact of polarization on infections, illness, and mortality during the pandemic; offer a psychological analysis of key policy questions; and identify a set of future research questions for scholars and policy experts. Our analysis suggests that the catastrophic death toll in the United States was largely preventable and due, in large part, to the polarization of the pandemic. Finally, we discuss implications for public policy to help avoid the same deadly mistakes in future public-health crises.

Algorithm-mediated social learning in online social networks

Human social learning is increasingly occurring on online social platforms, such as Twitter, Facebook, and TikTok. On these platforms, algorithms exploit existing social-learning biases (i.e., towards prestigious, ingroup, moral, and emotional information, or 'PRIME' information) to sustain users' attention and maximize engagement. Here, we synthesize emerging insights into 'algorithm-mediated social learning' and propose a framework that examines its consequences in terms of functional misalignment. We suggest that, when social-learning biases are exploited by algorithms, PRIME information becomes amplified via human-algorithm interactions in the digital social environment in ways that cause social misperceptions and conflict, and spread misinformation. We discuss solutions for reducing functional misalignment, including algorithms promoting bounded diversification and increasing transparency of algorithmic amplification.

The social media discourse of engaged partisans is toxic even when politics are irrelevant

Prevailing theories of partisan incivility on social media suggest that it derives from disagreement about political issues or from status competition between groups. This study-which analyzes the commenting behavior of Reddit users across diverse cultural contexts (subreddits)-tests the alternative hypothesis that such incivility derives in large part from a selection effect: Toxic people are especially likely to opt into discourse in partisan contexts. First, we examined commenting behavior across over 9,000 unique cultural contexts (subreddits) and confirmed that discourse is indeed more toxic in partisan (e.g. r/progressive, r/conservatives) than in nonpartisan contexts (e.g. r/movies, r/programming). Next, we analyzed hundreds of millions of comments from over 6.3 million users and found robust evidence that: (i) the discourse of people whose behavior is especially toxic in partisan contexts is also especially toxic in nonpartisan contexts (i.e. people are not politics-only toxicity specialists); and (ii) when considering only nonpartisan contexts, the discourse of people who also comment in partisan contexts is more toxic than the discourse of people who do not. These effects were not driven by socialization processes whereby people overgeneralized toxic behavioral norms they had learned in partisan contexts. In contrast to speculation about the need for partisans to engage beyond their echo chambers, toxicity in nonpartisan contexts was higher among people who also comment in both left-wing and right-wing contexts (bilaterally engaged users) than among people who also comment in only left-wing or right-wing contexts (unilaterally engaged users). The discussion considers implications for democratic functioning and theories of polarization.

Emotional content and sharing on Facebook: A theory cage match

While emotional content predicts social media post sharing, competing theories of emotion imply different predictions about how emotional content will influence the virality of social media posts. We tested and compared these theoretical frameworks. Teams of annotators assessed more than 4000 multimedia posts from Polish and Lithuanian Facebook for more than 20 emotions. We found that, drawing on semantic space theory, modeling discrete emotions independently was superior to models examining valence (positive or negative), activation/arousal (high or low), or clusters of emotions and was on par with but had more explanatory power than a seven basic emotion model. Certain discrete emotions were associated with post sharing, including both positive and negative and relatively lower and higher activation/arousal emotions (e.g., amusement, cute/kama muta, anger, and sadness) even when controlling for number of followers, time up, topic, and Facebook angry reactions. These results provide key insights into better understanding of social media post virality.

Fun with Frustration? TikTok Influencers' Emotional Expression Predicts User Engagement with COVID-19 Vaccination Messages

This study examined what kinds of TikTok video and message features are associated with user engagement in the context of COVID-19 vaccination. Content analysis was applied to study a sample of 223 COVID-19 vaccination-related videos from creators with at least 10,000 followers. The content analysis involved coding themes, video formats, the valence of attitude toward vaccination, and emotional expressions from the influencers. A majority of videos showcased personal vaccination experiences, followed by fictitious dramas and instructional information. More fictitious dramas expressed unclear attitudes, neither explicitly supporting nor opposing the COVID-19 vaccine, compared to personal vaccination stories and instructional videos. Some imaginative and dramatic scenes, such as zombie transformation or dramatic spasms after taking the vaccines, were widely imitated across influencers, perhaps humorously, and raised concerns about diminishing positive images of vaccine uptake. Videos with simultaneous expression of humor and frustration significantly predicted engagement when the video content opposed or was uncertain about taking the vaccine, implying the effectiveness of mixed emotional attributes within a message. This study provides insight into how social context and message choices by creators interact to influence audience engagement.

People Think That Social Media Platforms Do (but Should Not) Amplify Divisive Content

Recent studies have documented the type of content that is most likely to spread widely, or go "viral," on social media, yet little is known about people's perceptions of what goes viral or what should go viral. This is critical to understand because there is widespread debate about how to improve or regulate social media algorithms. We recruited a sample of participants that is nationally representative of the U.S. population (according to age, gender, and race/ethnicity) and surveyed them about their perceptions of social media virality (n = 511). In line with prior research, people believe that divisive content, moral outrage, negative content, high-arousal content, and misinformation are all likely to go viral online. However, they reported that this type of content should not go viral on social media. Instead, people reported that many forms of positive content-such as accurate content, nuanced content, and educational content-are not likely to go viral even though they think this content should go viral. These perceptions were shared among most participants and were only weakly related to political orientation, social media usage, and demographic variables. In sum, there is broad consensus around the type of content people think social media platforms should and should not amplify, which can help inform solutions for improving social media.

Social Psychological Perspectives on Political Polarization: Insights and Implications for Climate Change

Political polarization is a barrier to enacting policy solutions to global issues. Social psychology has a rich history of studying polarization, and there is an important opportunity to define and refine its contributions to the present political realities. We do so in the context of one of the most pressing modern issues: climate change. We synthesize the literature on political polarization and its applications to climate change, and we propose lines of further research and intervention design. We focus on polarization in the United States, examining other countries when literature was available. The polarization literature emphasizes two types of mechanisms of political polarization: (1) individual-level psychological processes related to political ideology and (2) group-level psychological processes related to partisan identification. Interventions that address group-level processes can be more effective than those that address individual-level processes. Accordingly, we emphasize the promise of interventions leveraging superordinate identities, correcting misperceived norms, and having trusted leaders communicate about climate change. Behavioral interventions like these that are grounded in scientific research are one of our most promising tools to achieve the behavioral wedge that we need to address climate change and to make progress on other policy issues.

Unveiling the dynamics of emotions in society through an analysis of online social network conversations

Social networks can provide insights into the emotions expressed by a society. However, the dynamic nature of emotions presents a significant challenge for policymakers, politicians, and communication professionals who seek to understand and respond to changes in emotions over time. To address this challenge, this paper investigates the frequency, duration, and transition of 24 distinct emotions over a 2-year period, analyzing more than 5 million tweets. The study shows that emotions with lower valence but higher dominance and/or arousal are more prevalent in online social networks. Emotions with higher valence and arousal tend to last longer, while dominant emotions tend to have shorter durations. Emotions occupying the conversations predominantly inhibit others with similar valence and dominance, and higher arousal. Over a month, emotions with similar valences tend to prevail in online social network conversations.

Impact of Social Reference Cues on Misinformation Sharing on Social Media: Series of Experimental Studies

BACKGROUND

Health-related misinformation on social media is a key challenge to effective and timely public health responses. Existing mitigation measures include flagging misinformation or providing links to correct information, but they have not yet targeted social processes. Current approaches focus on increasing scrutiny, providing corrections to misinformation (debunking), or alerting users prospectively about future misinformation (prebunking and inoculation). Here, we provide a test of a complementary strategy that focuses on the social processes inherent in social media use, in particular, social reinforcement, social identity, and injunctive norms.

OBJECTIVE

This study aimed to examine whether providing balanced social reference cues (ie, cues that provide information on users sharing and, more importantly, not sharing specific content) in addition to flagging COVID-19-related misinformation leads to reductions in sharing behavior and improvement in overall sharing quality.

METHODS

A total of 3 field experiments were conducted on Twitter's native social media feed (via a newly developed browser extension). Participants' feed was augmented to include misleading and control information, resulting in 4 groups: no-information control, Twitter's own misinformation warning (misinformation flag), social cue only, and combined misinformation flag and social cue. We tracked the content shared or liked by participants. Participants were provided with social information by referencing either their personal network on Twitter or all Twitter users.

RESULTS

A total of 1424 Twitter users participated in 3 studies (n=824, n=322, and n=278). Across all 3 studies, we found that social cues that reference users' personal network combined with a misinformation flag reduced the sharing of misleading but not control information and improved overall sharing quality. We show that this improvement could be driven by a change in injunctive social norms (study 2) but not social identity (study 3).

CONCLUSIONS

Social reference cues combined with misinformation flags can significantly and meaningfully reduce the amount of COVID-19-related misinformation shared and improve overall sharing quality. They are a feasible and scalable way to effectively curb the sharing of COVID-19-related misinformation on social media.

Tokenization of social media engagements increases the sharing of false (and other) news but penalization moderates it

Some major social media companies are announcing plans to tokenize user engagements, derived from blockchain-based decentralized social media. This would bring financial and reputational incentives for engagement, which might lead users to post more objectionable content. Previous research showed that financial or reputational incentives for accuracy decrease the willingness to share misinformation. However, it is unclear to what extent such outcome would change if engagements instead of accuracy were incentivized, which is a more realistic scenario. To address this question, we conducted a survey experiment to examine the effects of hypothetical token incentives. We find that a simple nudge about the possibility of earning token-based points for the achieved user engagements increases the willingness to share different kinds of news, including misinformation. The presence of penalties for objectionable posts diminishes the positive effect of tokenization rewards on misinformation sharing, but it does not eliminate it. These results have policy implications for content moderation practices if platforms embrace decentralization and engagement tokenization.

Negative expressions are shared more on Twitter for public figures than for ordinary users

Social media users tend to produce content that contains more positive than negative emotional language. However, negative emotional language is more likely to be shared. To understand why, research has thus far focused on psychological processes associated with tweets' content. In the current study, we investigate if the content producer influences the extent to which their negative content is shared. More specifically, we focus on a group of users that are central to the diffusion of content on social media-public figures. We found that an increase in negativity was associated with a stronger increase in sharing for public figures compared to ordinary users. This effect was explained by two user characteristics, the number of followers and thus the strength of ties and the proportion of political tweets. The results shed light on whose negativity is most viral, allowing future research to develop interventions aimed at mitigating overexposure to negative content.

Mobilising Extremism in Times of Change: Analysing the UK's Far-Right Online Content During the Pandemic

The growing dissension towards the political handling of COVID-19, widespread job losses, backlash to extended lockdowns, and hesitancy surrounding the vaccine are propagating toxic far-right discourses in the UK. Moreover, the public is increasingly reliant on different social media platforms, including a growing number of participants on the far-right's fringe online networks, for all pandemic-related news and interactions. Therefore, with the proliferation of harmful far-right narratives and the public's reliance on these platforms for socialising, the pandemic environment is a breeding ground for radical ideologically-based mobilisation and social fragmentation. However, there remains a gap in understanding how these far-right online communities, during the pandemic, utilise societal insecurities to attract candidates, maintain viewership, and form a collective on social media platforms. The article aims to better understand online far-right mobilisation by examining, via a mixed-methodology qualitative content analysis and netnography, UK-centric content, narratives, and key political figures on the fringe platform, Gab. Through the dual-qualitative coding and analyses of 925 trending posts, the research outlines the platform's hate-filled media and the toxic nature of its communications. Moreover, the findings illustrate the far-right's online discursive dynamics, showcasing the dependence on Michael Hogg's uncertainty-identity mechanisms in the community's exploitation of societal insecurity. From these results, I propose a far-right mobilisation model termed Collective Anxiety, which illustrates that toxic communication is the foundation for the community's maintenance and recruitment. These observations set a precedent for hate-filled discourse on the platform and consequently have widespread policy implications that need addressing.

The role of the big geographic sort in online news circulation among U.S. Reddit users

Past research has attributed the circulation of online news to two main factors-individual characteristics (e.g., a person's information literacy) and social media effects (e.g., algorithm-mediated information diffusion)-and has overlooked a third one: the critical mass created by the offline self-segregation of Americans into like-minded geographical regions such as states (a phenomenon called 'The Big Sort'). We hypothesized that this latter factor matters for the online spreading of news not least because online interactions, despite having the potential of being global, end up being localized: interaction probability is known to rapidly decay with distance. Upon analysis of more than 8M Reddit comments containing news links spanning four years, from January 2016 to December 2019, we found that Reddit did not work as an 'hype machine' for news (as opposed to what previous work reported for other platforms, circulation was not mainly caused by platform-facilitated network effects). Rather, news circulation in Reddit worked as a supply-and-demand system: news items scaled linearly with the number of users in each state (with a scaling exponent [Formula: see text]  [Formula: see text], and a goodness of fit [Formula: see text]). Furthermore, deviations from such a universal pattern were best explained by state-level personality and cultural factors ([Formula: see text]), rather than socioeconomic conditions ([Formula: see text]) or political characteristics ([Formula: see text]). Higher-than-expected circulation of any type of news was found in states characterised by residents who tend to be less diligent in terms of their personality (low in conscientiousness) and by loose cultures understating the importance of adherence to norms (low in cultural tightness). Interestingly, the combination of those factors with low levels of education was then associated with the circulation of a particular type of news, that is, misinformation. These results suggest that online interactions are geographically bounded and, as such, news circulation cannot be studied purely as an Internet phenomenon but should be grounded into a user's offline cultural environment, which has become increasingly segregated over the decades, and is admittedly hard to change.

Twitter misogyny associated with Hillary Clinton increased throughout the 2016 U.S. election campaign

Online misogyny has become a fixture in female politicians' lives. Backlash theory suggests that it may represent a threat response prompted by female politicians' counterstereotypical, power-seeking behaviors. We investigated this hypothesis by analyzing Twitter references to Hillary Clinton before, during, and after her presidential campaign. We collected a corpus of over 9 million tweets from 2014 to 2018 that referred to Hillary Clinton, and employed an interrupted time series analysis on the relative frequency of misogynistic language within the corpus. Prior to 2015, the level of misogyny associated with Clinton decreased over time, but this trend reversed when she announced her presidential campaign. During the campaign, misogyny steadily increased and only plateaued after the election, when the threat of her electoral success had subsided. These findings are consistent with the notion that online misogyny towards female political nominees is a form of backlash prompted by their ambition for power in the political arena.

Negativity drives online news consumption

Online media is important for society in informing and shaping opinions, hence raising the question of what drives online news consumption. Here we analyse the causal effect of negative and emotional words on news consumption using a large online dataset of viral news stories. Specifically, we conducted our analyses using a series of randomized controlled trials (N = 22,743). Our dataset comprises ~105,000 different variations of news stories from Upworthy.com that generated ∼5.7 million clicks across more than 370 million overall impressions. Although positive words were slightly more prevalent than negative words, we found that negative words in news headlines increased consumption rates (and positive words decreased consumption rates). For a headline of average length, each additional negative word increased the click-through rate by 2.3%. Our results contribute to a better understanding of why users engage with online media.

Accuracy and social motivations shape judgements of (mis)information

The extent to which belief in (mis)information reflects lack of knowledge versus a lack of motivation to be accurate is unclear. Here, across four experiments (n = 3,364), we motivated US participants to be accurate by providing financial incentives for correct responses about the veracity of true and false political news headlines. Financial incentives improved accuracy and reduced partisan bias in judgements of headlines by about 30%, primarily by increasing the perceived accuracy of true news from the opposing party (d = 0.47). Incentivizing people to identify news that would be liked by their political allies, however, decreased accuracy. Replicating prior work, conservatives were less accurate at discerning true from false headlines than liberals, yet incentives closed the gap in accuracy between conservatives and liberals by 52%. A non-financial accuracy motivation intervention was also effective, suggesting that motivation-based interventions are scalable. Altogether, these results suggest that a substantial portion of people's judgements of the accuracy of news reflects motivational factors.

Winning! Election returns and engagement in social media

This article analyzes social media engagement when elections are adjudicated to one of the contending parties. We extend existing models of political dialogue to explain differences in social media engagement (i.e. time-to-retweet) when users support the winner or losers of an election. We show that users who support the winning candidate are more engaged and have a lower time-to-retweet. We also show heterogeneity in Twitter engagement conditional on the number of followers, with accounts with more followers being less sensitive to the election result. We measure the effect of electoral adjudication using a regression discontinuity design, with estimates by winning or losing status, and for accounts with many followers (high authority) or with few followers (low authority). Analyses use Twitter data collected in Argentina (2019), Brazil (2018), the United Kingdom (2019), and the United States (2016).

Hatemongers ride on echo chambers to escalate hate speech diffusion

Recent years have witnessed a swelling rise of hateful and abusive content over online social networks. While detection and moderation of hate speech have been the early go-to countermeasures, the solution requires a deeper exploration of the dynamics of hate generation and propagation. We analyze more than 32 million posts from over 6.8 million users across three popular online social networks to investigate the interrelations between hateful behavior, information dissemination, and polarized organization mediated by echo chambers. We find that hatemongers play a more crucial role in governing the spread of information compared to singled-out hateful content. This observation holds for both the growth of information cascades as well as the conglomeration of hateful actors. Dissection of the core-wise distribution of these networks points towards the fact that hateful users acquire a more well-connected position in the social network and often flock together to build up information cascades. We observe that this cohesion is far from mere organized behavior; instead, in these networks, hatemongers dominate the echo chambers-groups of users actively align themselves to specific ideological positions. The observed dominance of hateful users to inflate information cascades is primarily via user interactions amplified within these echo chambers. We conclude our study with a cautionary note that popularity-based recommendation of content is susceptible to be exploited by hatemongers given their potential to escalate content popularity via echo-chambered interactions.

The Psychology of Athletic Endeavor

A considerable amount of human behavior occurs within the context of sports. In recent years there have been notable advances in psychological science research applied to understanding athletic endeavor. This work has utilized a number of novel theoretical, methodological, and data analytic approaches. We review the current evidence related to developmental considerations, intrapersonal athlete factors, group processes, and the role of the coach in explaining how athletes function within the sport domain. This body of work sheds light on the diverse ways in which psychological processes contribute to athletic strivings. It also has the potential to spark interest in domains of psychology concerned with achievement as well as to encourage cross-domain fertilization of ideas.

Human-centricity in AI governance: A systemic approach

Human-centricity is considered a central aspect in the development and governance of artificial intelligence (AI). Various strategies and guidelines highlight the concept as a key goal. However, we argue that current uses of Human-Centered AI (HCAI) in policy documents and AI strategies risk downplaying promises of creating desirable, emancipatory technology that promotes human wellbeing and the common good. Firstly, HCAI, as it appears in policy discourses, is the result of aiming to adapt the concept of human-centered design (HCD) to the public governance context of AI but without proper reflection on how it should be reformed to suit the new task environment. Second, the concept is mainly used in reference to realizing human and fundamental rights, which are necessary, but not sufficient for technological emancipation. Third, the concept is used ambiguously in policy and strategy discourses, making it unclear how it should be operationalized in governance practices. This article explores means and approaches for using the HCAI approach for technological emancipation in the context of public AI governance. We propose that the potential for emancipatory technology development rests on expanding the traditional user-centered view of technology design to involve community- and society-centered perspectives in public governance. Developing public AI governance in this way relies on enabling inclusive governance modalities that enhance the social sustainability of AI deployment. We discuss mutual trust, transparency, communication, and civic tech as key prerequisites for socially sustainable and human-centered public AI governance. Finally, the article introduces a systemic approach to ethically and socially sustainable, human-centered AI development and deployment.

A systematic review of worldwide causal and correlational evidence on digital media and democracy

One of today's most controversial and consequential issues is whether the global uptake of digital media is causally related to a decline in democracy. We conducted a systematic review of causal and correlational evidence (N = 496 articles) on the link between digital media use and different political variables. Some associations, such as increasing political participation and information consumption, are likely to be beneficial for democracy and were often observed in autocracies and emerging democracies. Other associations, such as declining political trust, increasing populism and growing polarization, are likely to be detrimental to democracy and were more pronounced in established democracies. While the impact of digital media on political systems depends on the specific variable and system in question, several variables show clear directions of associations. The evidence calls for research efforts and vigilance by governments and civil societies to better understand, design and regulate the interplay of digital media and democracy.

Longitudinal analysis of sentiment and emotion in news media headlines using automated labelling with Transformer language models

This work describes a chronological (2000–2019) analysis of sentiment and emotion in 23 million headlines from 47 news media outlets popular in the United States. We use Transformer language models fine-tuned for detection of sentiment (positive, negative) and Ekman’s six basic emotions (anger, disgust, fear, joy, sadness, surprise) plus neutral to automatically label the headlines. Results show an increase of sentiment negativity in headlines across written news media since the year 2000. Headlines from right-leaning news media have been, on average, consistently more negative than headlines from left-leaning outlets over the entire studied time period. The chronological analysis of headlines emotionality shows a growing proportion of headlines denoting anger, fear, disgust and sadness and a decrease in the prevalence of emotionally neutral headlines across the studied outlets over the 2000–2019 interval. The prevalence of headlines denoting anger appears to be higher, on average, in right-leaning news outlets than in left-leaning news media.

Most users do not follow political elites on Twitter; those who do show overwhelming preferences for ideological congruity

We offer comprehensive evidence of preferences for ideological congruity when people engage with politicians, pundits, and news organizations on social media. Using 4 years of data (2016-2019) from a random sample of 1.5 million Twitter users, we examine three behaviors studied separately to date: (i) following of in-group versus out-group elites, (ii) sharing in-group versus out-group information (retweeting), and (iii) commenting on the shared information (quote tweeting). We find that the majority of users (60%) do not follow any political elites. Those who do follow in-group elite accounts at much higher rates than out-group accounts (90 versus 10%), share information from in-group elites 13 times more frequently than from out-group elites, and often add negative comments to the shared out-group information. Conservatives are twice as likely as liberals to share in-group versus out-group content. These patterns are robust, emerge across issues and political elites, and exist regardless of users' ideological extremity.

Social media sharing of low-quality news sources by political elites

Increased sharing of untrustworthy information on social media platforms is one of the main challenges of our modern information society. Because information disseminated by political elites is known to shape citizen and media discourse, it is particularly important to examine the quality of information shared by politicians. Here, we show that from 2016 onward, members of the Republican Party in the US Congress have been increasingly sharing links to untrustworthy sources. The proportion of untrustworthy information posted by Republicans versus Democrats is diverging at an accelerating rate, and this divergence has worsened since President Biden was elected. This divergence between parties seems to be unique to the United States as it cannot be observed in other western democracies such as Germany and the United Kingdom, where left-right disparities are smaller and have remained largely constant.

Interventions to reduce partisan animosity

Rising partisan animosity is associated with a reduction in support for democracy and an increase in support for political violence. Here we provide a multi-level review of interventions designed to reduce partisan animosity, which we define as negative thoughts, feelings and behaviours towards a political outgroup. We introduce the TRI framework to capture three levels of intervention-thoughts (correcting misconceptions and highlighting commonalities), relationships (building dialogue skills and fostering positive contact) and institutions (changing public discourse and transforming political structures)-and connect these levels by highlighting the importance of motivation and mobilization. Our review encompasses both interventions conducted as part of academic research projects and real-world interventions led by practitioners in non-profit organizations. We also explore the challenges of durability and scalability, examine self-fulfilling polarization and interventions that backfire, and discuss future directions for reducing partisan animosity.

Social media behavior is associated with vaccine hesitancy

Understanding how vaccine hesitancy relates to online behavior is crucial for addressing current and future disease outbreaks. We combined survey data measuring attitudes toward the COVID-19 vaccine with Twitter data in two studies (N 1 = 464 Twitter users, N 2 = 1,600 Twitter users) with preregistered hypotheses to examine how real-world social media behavior is associated with vaccine hesitancy in the United States (US) and the United Kingdom (UK). In Study 1, we found that following the accounts of US Republican politicians or hyper-partisan/low-quality news sites were associated with lower confidence in the COVID-19 vaccine-even when controlling for key demographics such as self-reported political ideology and education. US right-wing influencers (e.g. Candace Owens, Tucker Carlson) had followers with the lowest confidence in the vaccine. Network analysis revealed that participants who were low and high in vaccine confidence separated into two distinct communities (or "echo chambers"), and centrality in the more right-wing community was associated with vaccine hesitancy in the US, but not in the UK. In Study 2, we found that one's likelihood of not getting the vaccine was associated with retweeting and favoriting low-quality news websites on Twitter. Altogether, we show that vaccine hesitancy is associated with following, sharing, and interacting with low-quality information online, as well as centrality within a conservative-leaning online community in the US. These results illustrate the potential challenges of encouraging vaccine uptake in a polarized social media environment.

Cognitive-motivational mechanisms of political polarization in social-communicative contexts

Healthy democratic polities feature competing visions of a good society but also require some level of cooperation and institutional trust. Democracy is at risk when citizens become so polarized that an 'us versus them' mentality dominates. Despite a vast multidisciplinary literature, no coherent conceptual framework of the microlevel dynamics that increase or decrease polarization has been presented. In this Review, we provide a conceptual framework to integrate scientific knowledge about cognitive-motivational mechanisms that influence political polarization and the social-communicative contexts in which they are enacted. Ego-justifying and group-justifying motives lead individuals to defend their own pre-existing beliefs and those of their in-group, respectively. However, a distinct class of system-justifying motives contributes to asymmetric forms of polarization. Whereas conservative-rightist ideology is associated with valuing tradition, social order and maintenance of the status quo, liberal-leftist ideology is associated with a push for egalitarian social change. These cognitive-motivational mechanisms interact with social influence processes linked to communication source, message and channel factors, all of which might contribute to increased or decreased polarization. We conclude with a discussion of unanswered questions and ways in which our framework can be extended to the study of culture and institutions.

Platform Effects on Public Health Communication: A Comparative and National Study of Message Design and Audience Engagement Across Twitter and Facebook

BACKGROUND

Public health agencies widely adopt social media for health and risk communication. Moreover, different platforms have different affordances, which may impact the quality and nature of the messaging and how the public engages with the content. However, these platform effects are not often compared in studies of health and risk communication and not previously for the COVID-19 pandemic.

OBJECTIVE

This study measures the potential media effects of Twitter and Facebook on public health message design and engagement by comparing message elements and audience engagement in COVID-19-related posts by local, state, and federal public health agencies in the United States during the pandemic, to advance theories of public health messaging on social media and provide recommendations for tailored social media communication strategies.

METHODS

We retrieved all COVID-19-related posts from major US federal agencies related to health and infectious disease, all major state public health agencies, and selected local public health departments on Twitter and Facebook. A total of 100,785 posts related to COVID-19, from 179 different accounts of 96 agencies, were retrieved for the entire year of 2020. We adopted a framework of social media message elements to analyze the posts across Facebook and Twitter. For manual content analysis, we subsampled 1677 posts. We calculated the prevalence of various message elements across the platforms and assessed the statistical significance of differences. We also calculated and assessed the association between message elements with normalized measures of shares and likes for both Facebook and Twitter.

RESULTS

Distributions of message elements were largely similar across both sites. However, political figures (P<.001), experts (P=.01), and nonpolitical personalities (P=.01) were significantly more present on Facebook posts compared to Twitter. Infographics (P<.001), surveillance information (P<.001), and certain multimedia elements (eg, hyperlinks, P<.001) were more prevalent on Twitter. In general, Facebook posts received more (normalized) likes (0.19%) and (normalized) shares (0.22%) compared to Twitter likes (0.08%) and shares (0.05%). Elements with greater engagement on Facebook included expressives and collectives, whereas posts related to policy were more engaged with on Twitter. Science information (eg, scientific explanations) comprised 8.5% (73/851) of Facebook and 9.4% (78/826) of Twitter posts. Correctives of misinformation only appeared in 1.2% (11/851) of Facebook and 1.4% (12/826) of Twitter posts.

CONCLUSIONS

In general, we find a data and policy orientation for Twitter messages and users and a local and personal orientation for Facebook, although also many similarities across platforms. Message elements that impact engagement are similar across platforms but with some notable distinctions. This study provides novel evidence for differences in COVID-19 public health messaging across social media sites, advancing knowledge of public health communication on social media and recommendations for health and risk communication strategies on these online platforms.

The Asymmetric Influence of Emotion in the Sharing of COVID-19 Science on Social Media: Observational Study

BACKGROUND

Unlike past pandemics, COVID-19 is different to the extent that there is an unprecedented surge in both peer-reviewed and preprint research publications, and important scientific conversations about it are rampant on online social networks, even among laypeople. Clearly, this new phenomenon of scientific discourse is not well understood in that we do not know the diffusion patterns of peer-reviewed publications vis-à-vis preprints and what makes them viral.

OBJECTIVE

This paper aimed to examine how the emotionality of messages about preprint and peer-reviewed publications shapes their diffusion through online social networks in order to inform health science communicators' and policy makers' decisions on how to promote reliable sharing of crucial pandemic science on social media.

METHODS

We collected a large sample of Twitter discussions of early (January to May 2020) COVID-19 medical research outputs, which were tracked by Altmetric, in both preprint servers and peer-reviewed journals, and conducted statistical analyses to examine emotional valence, specific emotions, and the role of scientists as content creators in influencing the retweet rate.

RESULTS

Our large-scale analyses (n=243,567) revealed that scientific publication tweets with positive emotions were transmitted faster than those with negative emotions, especially for messages about preprints. Our results also showed that scientists' participation in social media as content creators could accentuate the positive emotion effects on the sharing of peer-reviewed publications.

CONCLUSIONS

Clear communication of critical science is crucial in the nascent stage of a pandemic. By revealing the emotional dynamics in the social media sharing of COVID-19 scientific outputs, our study offers scientists and policy makers an avenue to shape the discussion and diffusion of emerging scientific publications through manipulation of the emotionality of tweets. Scientists could use emotional language to promote the diffusion of more reliable peer-reviewed articles, while avoiding using too much positive emotional language in social media messages about preprints if they think that it is too early to widely communicate the preprint (not peer reviewed) data to the public.

Comparative analysis of social bots and humans during the COVID-19 pandemic

Using more than 4 billion tweets and labels on more than 5 million users, this paper compares the behavior of humans and bots politically and semantically during the pandemic. Results reveal liberal bots are more central than humans in general, but less important than institutional humans as the elite circle grows smaller. Conservative bots are surprisingly absent when compared to prior work on political discourse, but are better than liberal bots at eliciting replies from humans, which suggest they may be perceived as human more frequently. In terms of topic and framing, conservative humans and bots disproportionately tweet about the Bill Gates and bio-weapons conspiracy, whereas the 5G conspiracy is bipartisan. Conservative humans selectively ignore mask-wearing and we observe prevalent out-group tweeting when discussing policy. We discuss and contrast how humans appear more centralized in health-related discourse as compared to political events, which suggests the importance of credibility and authenticity for public health in online information diffusion.