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In response to intense pressure, technology companies have enacted policies to combat misinformation1-4. The enforcement of these policies has, however, led to technology companies being regularly accused of political bias5-7. We argue that differential sharing of misinformation by people identifying with different political groups8-15 could lead to political asymmetries in enforcement, even by unbiased policies. We first analysed 9,000 politically active Twitter users during the US 2020 presidential election. Although users estimated to be pro-Trump/conservative were indeed substantially more likely to be suspended than those estimated to be pro-Biden/liberal, users who were pro-Trump/conservative also shared far more links to various sets of low-quality news sites-even when news quality was determined by politically balanced groups of laypeople, or groups of only Republican laypeople-and had higher estimated likelihoods of being bots. We find similar associations between stated or inferred conservatism and low-quality news sharing (on the basis of both expert and politically balanced layperson ratings) in 7 other datasets of sharing from Twitter, Facebook and survey experiments, spanning 2016 to 2023 and including data from 16 different countries. Thus, even under politically neutral anti-misinformation policies, political asymmetries in enforcement should be expected. Political imbalance in enforcement need not imply bias on the part of social media companies implementing anti-misinformation policies.

Automated social media accounts, known as bots, have been shown to spread disinformation and manipulate online discussions. We study the behavior of retweet bots on Twitter during the first impeachment of U.S. President Donald Trump. We collect over 67.7 million impeachment related tweets from 3.6 million users, along with their 53.6 million edge follower network. We find although bots represent 1% of all users, they generate over 31% of all impeachment related tweets. We also find bots share more disinformation, but use less toxic language than other users. Among supporters of the Qanon conspiracy theory, a popular disinformation campaign, bots have a prevalence near 10%. The follower network of Qanon supporters exhibits a hierarchical structure, with bots acting as central hubs surrounded by isolated humans. We quantify bot impact using the generalized harmonic influence centrality measure. We find there are a greater number of pro-Trump bots, but on a per bot basis, anti-Trump and pro-Trump bots have similar impact, while Qanon bots have less impact. This lower impact is due to the homophily of the Qanon follower network, suggesting this disinformation is spread mostly within online echo-chambers.

To investigate if a behavioral nudge comprising a vaccination opportunity that employs a comparative probe first (i.e., which vaccine to take) versus the more commonly-used deliberative probe (i.e., willingness to take a vaccine), reduces vaccine hesitancy, while controlling for political partisanship. In a randomized study, conducted on Amazon Mechanical Turk and Prolific, we varied the manner in which the vaccination offer is posed. In one group, participants were asked to compare which vaccine they would like to take (i.e., the comparative probe), while, in another group, participants were asked to deliberate whether they would like to take the vaccine (i.e., the deliberative probe). Participants' political preferences were also measured. The primary outcome variable was vaccine hesitancy. A LOGIT regression (N = 1736), was conducted to test the research questions. Overall, the comparative probe yielded a 6% reduction in vaccine hesitancy relative to the typical deliberative probe. Additionally, while vaccine hesitancy varies due to individual political views, the comparative probe is effective at reducing vaccine hesitancy even among the most vaccine hesitant population (i.e., Pro-Trump Republicans) by almost 10% on average. Subtly changing the manner in which the vaccination offer is framed, by asking people to compare which vaccine to take, and not deliberate about whether they would like to take a vaccine, can reduce vaccine hesitancy, without being psychologically taxing or curtailing individuals' freedom to choose. The nudge is especially effective among highly vaccine hesitant populations such as Pro-Trump Republicans. Our results suggest a costless communication protocol in face-to-face interactions on doorsteps, in clinics, in Pro-Trump regions and in the mass media, that might protect 5 million Americans from COVID-19.

This study examines the presence and role of Coordinated Link Sharing Behavior (CLSB) on Facebook around the "America's Frontline Doctors" press conference, and the promotion of several unproven conspiracy theories including the false assertion that hydroxychloroquine is a "cure" for COVID-19 by Dr. Stella Immanuel, one of the doctors who took part in the press conference. We collected 7,737 public Facebook posts mentioning Stella Immanuel using CrowdTangle and then applied the specialized program CooRnet to detect CLSB among Facebook public pages, groups and verified profiles. Finally, we used a mixed-method approach consisting of both network and content analysis to examine the nature and scope of the detected CLSB. Our analysis shows how Facebook accounts engaged in CLSB to fuel the spread of misinformation. We identified a coalition of Facebook accounts that engaged in CLSB to promote COVID-19 related misinformation. This coalition included US-based pro-Trump, QAnon, and anti-vaccination accounts. In addition, we identified Facebook accounts that engaged in CLSB in other countries, such as Brazil and France, that primarily promoted hydroxychloroquine, and some accounts in African countries that criticized the government's pandemic response in their countries.

Decades of research have demonstrated that we like people who are more similar to us. The present research tested a potential mechanism for this similarity-liking effect in the domain of politics: the stereotype that people's political orientation reflects their morals. People believe that Democrats are more likely to endorse individualizing morals like fairness and Republicans are more likely to endorse binding morals like obedience to authority. Prior to the 2016 election, American participants (N = 314) viewed an ostensible Facebook profile that shared an article endorsing conservative ideals (pro-Trump or pro-Republican), or liberal ideals (pro-Clinton or pro-Democrat). Participants rated the favorability of the profile-owner, and completed the Moral Foundations Questionnaire for the profile-owner and themselves. As predicted, participants liked the profile-owner more when they shared political beliefs, and used political stereotypes to infer the moral foundations of the profile-owner. Additionally, the perceived moral foundation endorsement of the profile owner differentially mediated the relationship between the ideology and evaluations of the profile owner based on the party affiliation of the participant: perceived individualizing foundations mediated the relationship for Democratic participants and perceived binding foundations mediated the relationship for Republican participants. In other words, people liked their in-group members more because they thought that the profile-owner endorsed a specific type of morals. In Study 2 (N = 486), we ruled out the potential explanation that any political stereotype can account for the similarity-liking effect, replicating the results of Study 1 even when controlling for perceptions of other personality differences. Taken together, these studies highlight that there may be something unique about the perceived type of morality of political in-group and out-group members that may be contributing to the similarity-liking effect in politics.

Objectives. To understand how Twitter accounts operated by the Russian Internet Research Agency (IRA) discussed vaccines to increase the credibility of their manufactured personas.Methods. We analyzed 2.82 million tweets published by 2689 IRA accounts between 2015 and 2017. Combining unsupervised machine learning and network analysis to identify "thematic personas" (i.e., accounts that consistently share the same topics), we analyzed the ways in which each discussed vaccines.Results. We found differences in volume and valence of vaccine-related tweets among 9 thematic personas. Pro-Trump personas were more likely to express antivaccine sentiment. Anti-Trump personas expressed support for vaccination. Others offered a balanced valence, talked about vaccines neutrally, or did not tweet about vaccines.Conclusions. IRA-operated accounts discussed vaccines in manners consistent with fabricated US identities.Public Health Implications. IRA accounts discussed vaccines online in ways that evoked political identities. This could exacerbate recently emerging partisan gaps relating to vaccine misinformation, as differently valenced messages were targeted at different segments of the US public. These sophisticated targeting efforts, if repeated and increased in reach, could reduce vaccination rates and magnify health disparities.

According to one recent review of the burgeoning interdisciplinary scholarly literature on populism, populism's "relationship with gender issues remains largely understudied" (Abi-Hassan, 2017, 426-427). Of those scholarly treatments that do exist, the lion's share focus on the role of men and masculinity in populist movements. In this essay, I argue scholarly reflection on the relationship of gender and populism should not be limited to this narrow frame. Through a close examination of the complex gender politics of QAnon, a pro-Trump conspiracy movement that burst into the mainstream of U.S. politics and culture with the onset of the global Coronavirus pandemic, I demonstrate that populist deployments of femininity are as rich, complex, and potent as their deployments of masculinity. QAnon, I argue, is a case study in how femininity, particularly feminine identities centered on motherhood and maternal duty, can be mobilized to engage women in populist political projects. Until scholars of populism start asking Cynthia Enloe's famous question, "Where are the women?," in a sustained and rigorous way, phenomena that are integral to populism's functioning will elude us and our understanding of the relationship between gender and populism will remain partial and incomplete (Enloe, 2014).

Researchers might assume that neutrality does not shape thought and action because it signals that nothing in the environment needs attention, hence a person has little need to alter their behavior. However, feeling neutral about an issue might be consequential. The COVID-19 pandemic was a major issue during the 2020 U.S. presidential election. We examined whether feeling neutral about COVID-19 was associated with attitudes about the top 2 presidential candidates (Trump vs. Biden) and behavior (i.e., whether a person voted and who they voted for). Data were collected at 2 critical time points: Study 1 was conducted immediately after the U.S. presidential election and Study 2 was conducted prior to the second Senate impeachment trial of Trump. Because feeling neutral about COVID-19 might indicate that a person views the issue as unworthy of attention, a perspective more aligned with Trump's approach, we hypothesized that feeling neutral about COVID-19 would be associated with more pro-Trump attitudes and behaviors. Even after accounting for other affects about COVID-19, in both studies, neutrality was associated with more favorable attitudes toward Trump, less favorable attitudes toward Biden, being less likely to vote, and if a person did vote, being more likely to vote for Trump. In Sudy 2, neutrality was associated with less support for impeaching Trump. Overall, in contrast to the view that neutral affect exerts little influence, neutrality can be critically intertwined with thought and action. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

So-called "fake news" has renewed concerns about the prevalence and effects of misinformation in political campaigns. Given the potential for widespread dissemination of this material, we examine the individual-level characteristics associated with sharing false articles during the 2016 U.S. presidential campaign. To do so, we uniquely link an original survey with respondents' sharing activity as recorded in Facebook profile data. First and foremost, we find that sharing this content was a relatively rare activity. Conservatives were more likely to share articles from fake news domains, which in 2016 were largely pro-Trump in orientation, than liberals or moderates. We also find a strong age effect, which persists after controlling for partisanship and ideology: On average, users over 65 shared nearly seven times as many articles from fake news domains as the youngest age group.

Numerous polls suggest that COVID-19 is a profoundly partisan issue in the United States. Using the geotracking data of 15 million smartphones per day, we found that US counties that voted for Donald Trump (Republican) over Hillary Clinton (Democrat) in the 2016 presidential election exhibited 14% less physical distancing between March and May 2020. Partisanship was more strongly associated with physical distancing than numerous other factors, including counties' COVID-19 cases, population density, median income, and racial and age demographics. Contrary to our predictions, the observed partisan gap strengthened over time and remained when stay-at-home orders were active. Additionally, county-level consumption of conservative media (Fox News) was related to reduced physical distancing. Finally, the observed partisan differences in distancing were associated with subsequently higher COVID-19 infection and fatality growth rates in pro-Trump counties. Taken together, these data suggest that US citizens' responses to COVID-19 are subject to a deep-and consequential-partisan divide.

Allegations that TikTok shadow bans political content shape what creators post, what advertisers fund, and how regulators act, yet they are hard to adjudicate because platforms do not disclose how content is ranked. We test the claim with a dense hourly panel of 556,946 follower-normalized views across 2,753 videos from 67 accounts curated into pro and anti sides of three contested topics (U.S. immigration enforcement, Trump coverage, and Israel/Palestine). On-topic videos are identified by a multi-step classifier, and stance is taken from each account's curated side. The conventional analysis appears to answer yes. Pooling the hourly snapshots, the topic-conditional reach gap reaches p < 10^-140. Analyzed at the account level, the independent unit at which we sample and assign stance, the gap disappears. Every account-level reach contrast is null after correction (BH-FDR q near 0.9). We find no evidence of moderate-to-large reach suppression on any topic. The null is informative. Account-level confidence intervals and a power analysis rule out such effects. As a design check, the same framework detects a clear asymmetry on a different outcome. Oppositional content (anti-Trump,

Amidst the rising capabilities of generative AI to mimic specific human styles, this study investigates the ability of state-of-the-art large language models (LLMs), including GPT-4o, Gemini 1.5 Pro, and Claude Sonnet 3.5, to emulate the authorial signatures of prominent literary and political figures: Walt Whitman, William Wordsworth, Donald Trump, and Barack Obama. Utilizing a zero-shot prompting framework with strict thematic alignment, we generated synthetic corpora evaluated through a complementary framework combining transformer-based classification (BERT) and interpretable machine learning (XGBoost). Our methodology integrates Linguistic Inquiry and Word Count (LIWC) markers, perplexity, and readability indices to assess the divergence between AI-generated and human-authored text. Results demonstrate that AI-generated mimicry remains highly detectable, with XGBoost models trained on a restricted set of eight stylometric features achieving accuracy comparable to high-dimensional neural classifiers. Feature importance analyses identify perplexity as the primary discriminative metric, revealing a significant divergence in the stochastic regularity of AI outputs compared to the

Large Language Models inherit stereotypes from their pretraining data, leading to biased behavior toward certain social groups in many Natural Language Processing tasks, such as hateful speech detection or sentiment analysis. Surprisingly, the evaluation of this kind of bias in stance detection methods has been largely overlooked by the community. Stance Detection involves labeling a statement as being against, in favor, or neutral towards a specific target and is among the most sensitive NLP tasks, as it often relates to political leanings. In this paper, we focus on the bias of Large Language Models when performing stance detection in a zero-shot setting. We automatically annotate posts in pre-existing stance detection datasets with two attributes: dialect or vernacular of a specific group and text complexity/readability, to investigate whether these attributes influence the model's stance detection decisions. Our results show that LLMs exhibit significant stereotypes in stance detection tasks, such as incorrectly associating pro-marijuana views with low text complexity and African American dialect with opposition to Donald Trump.

Automated social media accounts, known as bots, have been shown to spread disinformation and manipulate online discussions. We study the behavior of retweet bots on Twitter during the first impeachment of U.S. President Donald Trump. We collect over 67.7 million impeachment related tweets from 3.6 million users, along with their 53.6 million edge follower network. We find although bots represent 1% of all users, they generate over 31% of all impeachment related tweets. We also find bots share more disinformation, but use less toxic language than other users. Among supporters of the Qanon conspiracy theory, a popular disinformation campaign, bots have a prevalence near 10%. The follower network of Qanon supporters exhibits a hierarchical structure, with bots acting as central hubs surrounded by isolated humans. We quantify bot impact using the generalized harmonic influence centrality measure. We find there are a greater number of pro-Trump bots, but on a per bot basis, anti-Trump and pro-Trump bots have similar impact, while Qanon bots have less impact. This lower impact is due to the homophily of the Qanon follower network, suggesting this disinformation is spread mostly within on

As yet another alternative social network, Gettr positions itself as the "marketplace of ideas" where users should expect the truth to emerge without any administrative censorship. We looked deep inside the platform by analyzing it's structure, a sample of 6.8 million posts, and the responses from a sample of 124 Gettr users we interviewed to see if this actually is the case. Administratively, Gettr makes a deliberate attempt to stifle any external evaluation of the platform as collecting data is marred with unpredictable and abrupt changes in their API. Content-wise, Gettr notably hosts pro-Trump content mixed with conspiracy theories and attacks on the perceived "left." It's social network structure is asymmetric and centered around prominent right-thought leaders, which is characteristic for all alt-platforms. While right-leaning users joined Gettr as a result of a perceived freedom of speech infringement by the mainstream platforms, left-leaning users followed them in numbers as to "keep up with the misinformation." We contextualize these findings by looking into the Gettr's user interface design to provide a comprehensive insight into the incentive structure for joining and co

Widespread conspiracy theories may significantly impact our society. This paper focuses on the QAnon conspiracy theory, a consequential conspiracy theory that started on and disseminated successfully through social media. Our work characterizes how Reddit users who have participated in QAnon-focused subreddits engage in activities on the platform, especially outside their own communities. Using a large-scale Reddit moderation action against QAnon-related activities in 2018 as the source, we identified 13,000 users active in the early QAnon communities. We collected the 2.1 million submissions and 10.8 million comments posted by these users across all of Reddit from October 2016 to January 2021. The majority of these users were only active after the emergence of the QAnon Conspiracy theory and decreased in activity after Reddit's 2018 QAnon ban. A qualitative analysis of a sample of 915 subreddits where the "QAnon-enthusiastic" users were especially active shows that they participated in a diverse range of subreddits, often of unrelated topics to QAnon. However, most of the users' submissions were concentrated in subreddits that have sympathetic attitudes towards the conspiracy theo

Recent evidence has emerged linking coordinated campaigns by state-sponsored actors to manipulate public opinion on the Web. Campaigns revolving around major political events are enacted via mission-focused "trolls." While trolls are involved in spreading disinformation on social media, there is little understanding of how they operate, what type of content they disseminate, how their strategies evolve over time, and how they influence the Web's information ecosystem. In this paper, we begin to address this gap by analyzing 10M posts by 5.5K Twitter and Reddit users identified as Russian and Iranian state-sponsored trolls. We compare the behavior of each group of state-sponsored trolls with a focus on how their strategies change over time, the different campaigns they embark on, and differences between the trolls operated by Russia and Iran. Among other things, we find: 1) that Russian trolls were pro-Trump while Iranian trolls were anti-Trump; 2) evidence that campaigns undertaken by such actors are influenced by real-world events; and 3) that the behavior of such actors is not consistent over time, hence automated detection is not a straightforward task. Using the Hawkes Processe

A disputed exam, an unreliable detector, and one very late Apple Pages file

Scientists have created twisted laser beams that interact differently with right-handed and left-handed molecules, revealing their identity through the fragments they produce。 The approach could provide a faster, simpler, and more sensitive way to analyze important molecules used in chemistry and pharmaceuticals