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Understanding the individual-level characteristics associated with conspiracy theory beliefs is vital to addressing and combatting those beliefs. While researchers have identified numerous psychological and political characteristics associated with conspiracy theory beliefs, the generalizability of those findings is uncertain because they are typically drawn from studies of only a few conspiracy theories. Here, we employ a national survey of 2021 U.S. adults that asks about 15 psychological and political characteristics as well as beliefs in 39 different conspiracy theories. Across 585 relationships examined within both bivariate (correlations) and multivariate (regression) frameworks, we find that psychological traits (e.g., dark triad) and non-partisan/ideological political worldviews (e.g., populism, support for violence) are most strongly related to individual conspiracy theory beliefs, regardless of the belief under consideration, while other previously identified correlates (e.g., partisanship, ideological extremity) are inconsistently related. We also find that the correlates of specific conspiracy theory beliefs mirror those of conspiracy thinking (the predisposition), indicating that this predisposition operates like an ‘average’ of individual conspiracy theory beliefs. Overall, our findings detail the psychological and political traits of the individuals most drawn to conspiracy theories and have important implications for scholars and practitioners seeking to prevent or reduce the impact of conspiracy theories.

While a robust literature on the psychology of conspiracy theories has identified dozens of characteristics correlated with conspiracy theory beliefs, much less attention has been paid to understanding the generalized predisposition towards interpreting events and circumstances as the product of supposed conspiracies. Using a unique national survey of 2015 U.S. adults from October 2020, we investigate the relationship between this predisposition—conspiracy thinking—and 34 different psychological, political, and social correlates. Using conditional inference tree modeling—a machine learning-based approach designed to facilitate prediction using a flexible modeling methodology—we identify the characteristics that are most useful for orienting individuals along the conspiracy thinking continuum, including (but not limited to): anomie, Manicheanism, support for political violence, a tendency to share false information online, populism, narcissism, and psychopathy. Altogether, psychological characteristics are much more useful in predicting conspiracy thinking than are political and social characteristics, though even our robust set of correlates only partially accounts for variance in conspiracy thinking.

During the 2020 U.S. presidential election cycle, a combination of public statements and social media posts cast doubt on the legitimacy of the election. These sentiments flowed through various social networks and eventually sparked the January 6th insurrection at the Capitol. Here, we analyze both the network-level and content-level data that made the #StopTheSteal movement so effective online. We use Louvain clustering and a novel homogeneity metric to identify the most ideologically homogeneous groups within the discussion on the mainstream social network Twitter and alternative social network Parler. We show that these ideologically homogeneous groups spread messages further than their ideologically diverse counterparts. Our results also differentiate between ideologically homogeneous left- and right-leaning groups by measuring the characteristics of their texts, finding that right-leaning texts are stylistically similar to worldbuilding language that can be found in conspiracy theory texts.

Despite hundreds of studies examining belief in conspiracy theories, it is still unclear who— demographically —is most likely to believe such theories. To remedy this knowledge gap, we examine survey data containing various operationalizations of conspiracism across diverse sociopolitical contexts. Study 1 employs a 2021 U.S. survey (n = 2021) to examine associations between sociodemographic characteristics and beliefs in 39 conspiracy theories. Study 2 similarly employs a survey of 20 countries (n = 26,416) and 11 conspiracy theory beliefs. Study 3 reports results from a 2020 U.S. survey (n = 2015) measuring perceptions about which groups are engaging in conspiracies. Study 4 interrogates data from nine U.S. surveys (2012–2022; n = 14,334) to examine the relationships between sociodemographic characteristics and generalized conspiracy thinking. Study 5 synchronizes studies 1–4 to provide an intersectional analysis of conspiracy theory belief. Across studies, we observe remarkably consistent patterns: education, income, age (older), and White identification are negatively related to conspiracism, while Black identification is positively related. We conclude by discussing why conspiracy theories may appeal most to historically marginalized groups and how our findings can inform efforts to mitigate the negative effects of conspiracy theories.

In early 2021, groups of Reddit and Twitter users collaborated to raise the price of GameStop stock from $20 to $400 in a matter of days. The heavy influence of social media activity on the rise of GameStop prices can be contrasted with the muted social media influence on other, more traditional stocks. While traditional stocks are modeled quite successfully by current methods, such methods break down when used to model these so-called meme stocks. Our project analyzes the graph topology of retweet graphs built from GameStop-related tweets and other meme stocks to find that the clustering coefficient and network diameter of a retweet graph can be used to decrease the mean absolute error of meme stock trading volume predictions by as much as 46% over the control group during the first 70 trading days of 2021.