Publications

2024

December 11, 2024

PDF
Sandra González-Bailón, David Lazer, Pablo Barberá, William Godel, Hunt Allcott, Taylor Brown, Adriana Crespo-Tenorio, Deen Freelon, Matthew Gentzkow, Andrew M. Guess, Shanto Iyengar, Young Mie Kim, Neil Malhotra, Devra Moehler, Brendan Nyhan, Jennifer Pan, Carlos Velasco Rivera, Jaime Settle, Emily Thorson, Rebekah Tromble, Arjun Wilkins, Magdalena Wojcieszak, Chad Kiewiet de Jonge, Annie Franco, Winter Mason, Natalie Jomini Stroud, Joshua A. Tucker

Sociological Science

Abstract

+

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.

September 30, 2024

PDF
Mauricio Santillana, Ata A. Uslu, Tamanna Urmi, Alexi Quintana-Mathe, James N. Druckman, Katherine Ognyanova, Matthew Baum, Roy H. Perlis David Lazer

JAMA Network Open

Abstract

+

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.

August 25, 2024

PDF
Mauricio Santillana, Ata A. Uslu, Tamanna Urmi, Alexi Quintana, James N. Druckman, Katherine Ognyanova, Matthew Baum, Roy H. Perlis, David Lazer

medRxiv

Abstract

+

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.

August 24, 2024

PDF
Roy H. Perlis, Ata Uslu, Jonathan Schulman, Aliayah Himelfarb, Faith M. Gunning, Nili Solomonov, Mauricio Santillana, Matthew A. Baum, James N. Druckman, Katherine Ognyanova, David Lazer

Neuropsychopharmacology

Abstract

+

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.

June 5, 2024

PDF
Stefan D. McCabe, Diogo Ferrari, Jon Green, David M. J. Lazer, Kevin M. Esterling

Nature

Abstract

+

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 speech2,3. 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 users4,5 and natural experimental designs6,7, 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.

June 1, 2024

PDF
Alvaro Feal, Jeffrey Gleason, Pranav Goel, Jason Radford, Kai-Cheng Yang, John Basl, Michelle Meyer, David Choffnes, Christo Wilson, David Lazer

ICWSM Workshops

Abstract

+

The National Internet Observatory (NIO) aims to help researchers study online behavior. Participants install a browser extension and/or mobile apps to donate their online activity data along with comprehensive survey responses. The infrastructure will offer approved researchers access to a suite of structured, parsed content data for selected domains to enable analyses and understanding of Internet use in the US. This is all conducted within a robust research ethics framework, emphasizing ongoing informed consent and multiple layers, technical and legal, of interventions to protect the values at stake in data collection, data access, and research. This paper provides a brief overview of the NIO infrastructure, the data collected, the participants, and the researcher intake process.

May 29, 2024

PDF
Kai-Cheng Yang, Filippo Menczer

Journal of Quantitative Description: Digital Media

Abstract

+

Large language models (LLMs) exhibit impressive capabilities in generating realistic text across diverse subjects. Concerns have been raised that they could be utilized to produce fake content with a deceptive intention, although evidence thus far remains anecdotal. This paper presents a case study about a Twitter botnet that appears to employ ChatGPT to generate human-like content. Through heuristics, we identify 1,140 accounts and validate them via manual annotation. These accounts form a dense cluster of fake personas that exhibit similar behaviors, including posting machine-generated content and stolen images, and engage with each other through replies and retweets. ChatGPT-generated content promotes suspicious websites and spreads harmful comments. While the accounts in the AI botnet can be detected through their coordination patterns, current state-of-the-art LLM content classifiers fail to discriminate between them and human accounts in the wild. These findings highlight the threats posed by AI-enabled social bots.

February 15, 2024

PDF
Matthew David Simonson, Ray Block Jr, James N. Druckman, Katherine Ognyanova, David M. J. Lazer

Cambridge University Press

Abstract

+

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.

February 2, 2024

PDF
Sarah Shugars, Alexi Quintana-Mathé, Robin Lange, David Lazer

Journal of Computer-Mediated Communication

Abstract

+

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.