Publications

Social media play a pivotal role in disseminating web content, particularly during elections, yet our understanding of the association between demographic factors and political discourse online remains limited. Here, we introduce a unique dataset, DomainDemo, linking domains shared on Twitter (X) with the demographic characteristics of associated users, including age, gender, race, political affiliation, and geolocation, from 2011 to 2022. This new resource was derived from a panel of over 1.5 million Twitter users matched against their U.S. voter registration records, facilitating a better understanding of a decade of information flows on one of the most prominent social media platforms and trends in political and public discourse among registered U.S. voters from different sociodemographic groups. By aggregating user demographic information onto the domains, we derive five metrics that provide critical insights into over 129,000 websites. In particular, the localness and partisan audience metrics quantify the domains’ geographical reach and ideological orientation, respectively. These metrics show substantial agreement with existing classifications, suggesting the effectiveness and reliability of DomainDemo’s approach.

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Importance  Efforts to understand the complex association between social media use and mental health have focused on depression, with little investigation of other forms of negative affect, such as irritability and anxiety.

Objective  To characterize the association between self-reported use of individual social media platforms and irritability among US adults.

Design, Setting, and Participants  This survey study analyzed data from 2 waves of the COVID States Project, a nonprobability web-based survey conducted between November 2, 2023, and January 8, 2024, and applied multiple linear regression models to estimate associations with irritability. Survey respondents were aged 18 years and older.

Exposure  Self-reported social media use.

Main Outcomes and Measures  The primary outcome was score on the Brief Irritability Test (range, 5-30), with higher scores indicating greater irritability.

Results  Across the 2 survey waves, there were 42 597 unique participants, with mean (SD) age 46.0 (17.0) years; 24 919 (58.5%) identified as women, 17 222 (40.4%) as men, and 456 (1.1%) as nonbinary. In the full sample, 1216 (2.9%) identified as Asian American, 5939 (13.9%) as Black, 5322 (12.5%) as Hispanic, 624 (1.5%) as Native American, 515 (1.2%) as Pacific Islander, 28 354 (66.6%) as White, and 627 (1.5%) as other (ie, selecting the other option prompted the opportunity to provide a free-text self-description). In total, 33 325 (78.2%) of the survey respondents reported daily use of at least 1 social media platform, including 6037 (14.2%) using once a day, 16 678 (39.2%) using multiple times a day, and 10 610 (24.9%) using most of the day. Frequent use of social media was associated with significantly greater irritability in univariate regression models (for more than once a day vs never, 1.43 points [95% CI, 1.22-1.63 points]; for most of the day vs never, 3.37 points [95% CI, 3.15-3.60 points]) and adjusted models (for more than once a day, 0.38 points [95% CI, 0.18-0.58 points]; for most of the day, 1.55 points [95% CI, 1.32-1.78 points]). These associations persisted after incorporating measures of political engagement.

Conclusions and Relevance  In this survey study of 42 597 US adults, irritability represented another correlate of social media use that merits further characterization, in light of known associations with depression and suicidality.

Social media creates the possibility for rapid, viral spread of content, but how many posts actually reach millions? And is misinformation special in how it propagates? We answer these questions by analyzing the virality of and exposure to information on Facebook during the U.S. 2020 presidential election. We examine the diffusion trees of the approximately 1 B posts that were re-shared at least once by U.S.-based adults from July 1, 2020, to February 1, 2021. We differentiate misinformation from non-misinformation posts to show that (1) misinformation diffused more slowly, relying on a small number of active users that spread misinformation via long chains of peer-to-peer diffusion that reached millions; non-misinformation spread primarily through one-to-many affordances (mainly, Pages); (2) the relative importance of peer-to-peer spread for misinformation was likely due to an enforcement gap in content moderation policies designed to target mostly Pages and Groups; and (3) periods of aggressive content moderation proximate to the election coincide with dramatic drops in the spread and reach of misinformation and (to a lesser extent) political content.

Importance  Identifying and tracking new infections during an emerging pandemic is crucial to design and deploy interventions to protect populations and mitigate the pandemic’s effects, yet it remains a challenging task.

Objective  To characterize the ability of nonprobability online surveys to longitudinally estimate the number of COVID-19 infections in the population both in the presence and absence of institutionalized testing.

Design, Setting, and Participants  Internet-based online nonprobability surveys were conducted among residents aged 18 years or older across 50 US states and the District of Columbia, using the PureSpectrum survey vendor, approximately every 6 weeks between June 1, 2020, and January 31, 2023, for a multiuniversity consortium—the COVID States Project. Surveys collected information on COVID-19 infections with representative state-level quotas applied to balance age, sex, race and ethnicity, and geographic distribution.

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Importance Identifying and tracking new infections during an emerging pandemic is crucial to design and deploy interventions to protect populations and mitigate its effects, yet it remains a challenging task.

Objective To characterize the ability of non-probability online surveys to longitudinally estimate the number of COVID-19 infections in the population both in the presence and absence of institutionalized testing.

Design Internet-based non-probability surveys were conducted, using the PureSpectrum survey vendor, approximately every 6 weeks between April 2020 and January 2023. They collected information on COVID-19 infections with representative state-level quotas applied to balance age, gender, race and ethnicity, and geographic distribution. Data from this survey were compared to institutional case counts collected by Johns Hopkins University and wastewater surveillance data for SARS-CoV-2 from Biobot Analytics.

Setting Population-based online non-probability survey conducted for a multi-university consortium —the Covid States Project.

Participants Residents of age 18+ across 50 US states and the District of Columbia in the US.

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This study aimed to characterize the prevalence of irritability among U.S. adults, and the extent to which it co-occurs with major depressive and anxious symptoms. A non-probability internet survey of individuals 18 and older in 50 U.S. states and the District of Columbia was conducted between November 2, 2023, and January 8, 2024. Regression models with survey weighting were used to examine associations between the Brief Irritability Test (BITe5) and sociodemographic and clinical features. The survey cohort included 42,739 individuals, mean age 46.0 (SD 17.0) years; 25,001 (58.5%) identified as women, 17,281 (40.4%) as men, and 457 (1.1%) as nonbinary. A total of 1218(2.8%) identified as Asian American, 5971 (14.0%) as Black, 5348 (12.5%) as Hispanic, 1775 (4.2%) as another race, and 28,427 (66.5%) as white. Mean irritability score was 13.6 (SD 5.6) on a scale from 5 to 30. In linear regression models, irritability was greater among respondents who were female, younger, had lower levels of education, and lower household income. Greater irritability was associated with likelihood of thoughts of suicide in logistic regression models adjusted for sociodemographic features (OR 1.23, 95% CI 1.22–1.24). Among 1979 individuals without thoughts of suicide on the initial survey assessed for such thoughts on a subsequent survey, greater irritability was also associated with greater likelihood of thoughts of suicide being present (adjusted OR 1.17, 95% CI 1.12–1.23). The prevalence of irritability and its association with thoughts of suicide suggests the need to better understand its implications among adults outside of acute mood episodes.

The social media platforms of the twenty-first century have an enormous role in regulating speech in the USA and worldwide¹. However, there has been little research on platform-wide interventions on speech^2,3. Here we evaluate the effect of the decision by Twitter to suddenly deplatform 70,000 misinformation traffickers in response to the violence at the US Capitol on 6 January 2021 (a series of events commonly known as and referred to here as ‘January 6th’). Using a panel of more than 500,000 active Twitter users^4,5 and natural experimental designs^6,7, we evaluate the effects of this intervention on the circulation of misinformation on Twitter. We show that the intervention reduced circulation of misinformation by the deplatformed users as well as by those who followed the deplatformed users, though we cannot identify the magnitude of the causal estimates owing to the co-occurrence of the deplatforming intervention with the events surrounding January 6th. We also find that many of the misinformation traffickers who were not deplatformed left Twitter following the intervention. The results inform the historical record surrounding the insurrection, a momentous event in US history, and indicate the capacity of social media platforms to control the circulation of misinformation, and more generally to regulate public discourse.

Scholars have long recognized that interpersonal networks play a role in mobilizing social movements. Yet, many questions remain. This Element addresses these questions by theorizing about three dimensions of ties: emotionally strong or weak, movement insider or outsider, and ingroup or cross-cleavage. The survey data on the 2020 Black Lives Matter protests show that weak and cross-cleavage ties among outsiders enabled the movement to evolve from a small provocation into a massive national mobilization. In particular, the authors find that Black people mobilized one another through social media and spurred their non-Black friends to protest by sharing their personal encounters with racism. These results depart from the established literature regarding the civil rights movement that emphasizes strong, movement-internal, and racially homogenous ties. The networks that mobilize appear to have changed in the social media era. This title is also available as Open Access on Cambridge Core.

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Studies of gendered phenomena online have highlighted important disparities, such as who is likely to be elevated as an expert or face gender-based harassment. This research, however, typically relies upon inferring user gender—an act that perpetuates notions of gender as an easily observable, binary construct. Motivated by work in gender and queer studies, we therefore compare common approaches to gender inference in the context of online settings. We demonstrate that gender inference can have downstream consequences when studying gender inequities and find that nonbinary users are consistently likely to be misgendered or overlooked in analysis. In bringing a theoretical focus to this common methodological task, our contribution is in problematizing common measures of gender, encouraging researchers to think critically about what these constructs can and cannot capture, and calling for more research explicitly focused on gendered experiences beyond a binary.

Campaign contributions are a staple of congressional life. Yet, the search for tangible effects of congressional donations often focuses on the association between contributions and votes on congressional bills. We present an alternative approach by considering the relationship between money and legislators’ speech. Floor speeches are an important component of congressional behavior, and reflect a legislator’s policy priorities and positions in a way that voting cannot. Our research provides the first comprehensive analysis of the association between a legislator’s campaign donors and the policy issues they prioritize with congressional speech. Ultimately, we find a robust relationship between donors and speech, indicating a more pervasive role of money in politics than previously assumed. We use a machine learning framework on a new dataset that brings together legislator metadata for all representatives in the US House between 1995 and 2018, including committee assignments, legislative speech, donation records, and information about Political Action Committees. We compare information about donations against other potential explanatory variables, such as party affiliation, home state, and committee assignments, and find that donors consistently have the strongest association with legislators’ issue-attention. We further contribute a procedure for identifying speech and donation events that occur in close proximity to one another and share meaningful connections, identifying the proverbial needles in the haystack of speech and donation activity in Congress which may be cases of interest for investigative journalism. Taken together, our framework, data, and findings can help increase the transparency of the role of money in politics.