Publications

Importance: Marked elevation in levels of depressive symptoms compared with historical norms have been described during the COVID-19 pandemic, and understanding the extent to which these are associated with diminished in-person social interaction could inform public health planning for future pandemics or other disasters.

Objective: To describe the association between living in a US county with diminished mobility during the COVID-19 pandemic and self-reported depressive symptoms, while accounting for potential local and state-level confounding factors.

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Most prior and current research examining misinformation spread on social media focuses on reports published by 'fake' news sources. These approaches fail to capture another potential form of misinformation with a much larger audience: factual news from mainstream sources ('real' news) repurposed to promote false or misleading narratives. We operationalize narratives using an existing unsupervised NLP technique and examine the narratives present in misinformation content. We find that certain articles from reliable outlets are shared by a disproportionate number of users who also shared fake news on Twitter. We consider these 'real' news articles to be co-shared with fake news. We show that co-shared articles contain existing misinformation narratives at a significantly higher rate than articles from the same reliable outlets that are not co-shared with fake news. This holds true even when articles are chosen following strict criteria of reliability for the outlets and after accounting for the alternative explanation of partisan curation of articles. For example, we observe that a recent article published by The Washington Post titled "Vaccinated people now make up a majority of COVID deaths" was disproportionately shared by Twitter users with a history of sharing anti-vaccine false news reports. Our findings suggest a strategic repurposing of mainstream news by conveyors of misinformation as a way to enhance the reach and persuasiveness of misleading narratives. We also conduct a comprehensive case study to help highlight how such repurposing can happen on Twitter as a consequence of the inclusion of particular narratives in the framing of mainstream news.

Many critics raise concerns about the prevalence of ‘echo chambers’ on social media and their potential role in increasing political polarization. However, the lack of available data and the challenges of conducting large-scale field experiments have made it difficult to assess the scope of the problem^1,2. Here we present data from 2020 for the entire population of active adult Facebook users in the USA showing that content from ‘like-minded’ sources constitutes the majority of what people see on the platform, although political information and news represent only a small fraction of these exposures. To evaluate a potential response to concerns about the effects of echo chambers, we conducted a multi-wave field experiment on Facebook among 23,377 users for whom we reduced exposure to content from like-minded sources during the 2020 US presidential election by about one-third. We found that the intervention increased their exposure to content from cross-cutting sources and decreased exposure to uncivil language, but had no measurable effects on eight preregistered attitudinal measures such as affective polarization, ideological extremity, candidate evaluations and belief in false claims. These precisely estimated results suggest that although exposure to content from like-minded sources on social media is common, reducing its prevalence during the 2020 US presidential election did not correspondingly reduce polarization in beliefs or attitudes.

Over the July Fourth long weekend, people will pour into the small town of Gettysburg, Pennsylvania, to commemorate the 160th anniversary of one of the deadliest battles in U.S. history.

The three-day battle left over 50,000 Union and Confederate soldiers dead, wounded or missing and cemented Gettysburg’s place in American history as the turning point of the Civil War.

A few months after the battle, President Abraham Lincoln visited the town for the dedication of Soldiers’ National Cemetery. There, he delivered his famed Gettysburg Address. Lincoln called on Americans to dedicate themselves to the “unfinished work” for which so many at Gettysburg had died: the preservation of the United States and a “new birth of freedom” for the nation.

I have researched Americans’ support for political violence in my work as a political scientist at Northeastern and Harvard Universities. As an incoming professor at Gettysburg College, which was attacked by Confederate soldiers and served as a makeshift hospital during the battle, I wanted to see whether the legacies of the Civil War still affected Americans’ support for political violence today.

If popular online platforms systematically expose their users to partisan and unreliable news, they could potentially contribute to societal issues such as rising political polarization^1,2. This concern is central to the ‘echo chamber’^3,4,5 and ‘filter bubble’^6,7 debates, which critique the roles that user choice and algorithmic curation play in guiding users to different online information sources^8,9,10. These roles can be measured as exposure, defined as the URLs shown to users by online platforms, and engagement, defined as the URLs selected by users. However, owing to the challenges of obtaining ecologically valid exposure data—what real users were shown during their typical platform use—research in this vein typically relies on engagement data^{4,8,11,12,13,14,15,16} or estimates of hypothetical exposure^{17,18,19,20,21,22,23}. Studies involving ecological exposure have therefore been rare, and largely limited to social media platforms^7,24, leaving open questions about web search engines. To address these gaps, we conducted a two-wave study pairing surveys with ecologically valid measures of both exposure and engagement on Google Search during the 2018 and 2020 US elections. In both waves, we found more identity-congruent and unreliable news sources in participants’ engagement choices, both within Google Search and overall, than they were exposed to in their Google Search results. These results indicate that exposure to and engagement with partisan or unreliable news on Google Search are driven not primarily by algorithmic curation but by users’ own choices.

Importance: Post-COVID-19 condition (PCC), or long COVID, has become prevalent. The course of this syndrome, and likelihood of remission, has not been characterized. Objective: To quantify the rates of remission of PCC, and the sociodemographic features associated with remission. Design: 16 waves of a 50-state U.S. non-probability internet survey conducted between August 2020 and November 2022 Setting: Population-based Participants: Survey respondents age 18 and older Main Outcome and Measure: PCC remission, defined as reporting full recovery from COVID-19 symptoms among individuals who on a prior survey wave reported experiencing continued COVID-19 symptoms beyond 2 months after the initial month of symptoms. Results: Among 423 survey respondents reporting continued symptoms more than 2 months after acute test-confirmed COVID-19 illness, who then completed at least 1 subsequent survey, mean age was 53.7 (SD 13.6) years; 293 (69%) identified as women, and 130 (31%) as men; 9 (2%) identified as Asian, 29 (7%) as Black, 13 (3%) as Hispanic, 15 (4%) as another category including Native American or Pacific Islander, and the remaining 357 (84%) as White. Overall, 131/423 (31%) of those who completed a subsequent survey reported no longer being symptomatic. In Cox regression models, male gender, younger age, lesser impact of PCC symptoms at initial visit, and infection when the Omicron strain predominated were all statistically significantly associated with greater likelihood of remission; presence of brain fog or shortness of breath were associated with lesser likelihood of remission

After misinformation has been corrected, people initially update their belief extremely well. However, this change is rarely sustained over time, with belief returning towards pre-correction levels. This is called belief regression. The current study aimed to examine the association between memory for the correction and belief regression, and whether corrected misinformation suffers from belief regression more than affirmed facts. Participants from Prolific Academic (N = 612) rated the veracity of 16 misinformation and 16 factual items and were randomly assigned to a correction condition or test-retest control. Immediately after misinformation was corrected and facts affirmed, participants re-rated their belief and were asked whether they could remember the items' presented veracity. Participants repeated this post-test one month later. We found that belief and memory were highly associated, both immediately (⍴ = 0.51), and after one month (⍴ = 0.82), and that memory explained 66% of the variance in belief regression after correcting for measurement reliability. We found the rate of dissenting (accurately remembering that misinformation was presented as false but still believing it) remained stable between the immediate and delayed post-test, while the rate of forgetting quadrupled. After one month, 57% of participants who believed in the misinformation thought that the items were presented to them as true. Belief regression was more pronounced for misinformation than facts, but this was greatly attenuated once pre-test belief was equated. Together, these results clearly indicate that memory plays a fundamental role in belief regression, and that repeated corrections could be an effective method to counteract this phenomenon.

The COVID-19 pandemic offers an unprecedented natural experiment providing insights into the emergence of collective behavioral changes of both exogenous (government mandated) and endogenous (spontaneous reaction to infection risks) origin. Here, we characterize collective physical distancing -- mobility reductions, minimization of contacts, shortening of contact duration -- in response to the COVID-19 pandemic in the pre-vaccine era by analyzing de-identified, privacy-preserving location data for a panel of over 5.5 million anonymized, opted-in U.S. devices. We define five indicators of users' mobility and proximity to investigate how the emerging collective behavior deviates from the typical pre-pandemic patterns during the first nine months of the COVID-19 pandemic. We analyze both the dramatic changes due to the government mandated mitigation policies and the more spontaneous societal adaptation into a new (physically distanced) normal in the fall 2020. The indicators defined here allow the quantification of behavior changes across the rural/urban divide and highlight the statistical association of mobility and proximity indicators with metrics characterizing the pandemic's social and public health impact such as unemployment and deaths. This study provides a framework to study massive social distancing phenomena with potential uses in analyzing and monitoring the effects of pandemic mitigation plans at the national and international level.

Elite surveys are increasingly common in political science, but how best to motivate participation in them remains poorly understood. This study compares the effect of three treatments designed to increase participation in an online survey of international non-profit professionals: a monetary reward, an altruistic appeal emphasizing the study’s benefits, and a promise to give the respondent access to the study’s results. Only the monetary incentive increased the survey response rate. It did not decrease response quality as measured in terms of straight-lining or skipped questions, although it may have produced a pool of respondents more likely to speed through the survey. The findings suggest that monetary incentives reduce total survey error even in the context of an elite survey, perhaps especially with elite populations frequently contacted by researchers. However, such incentives may not be without trade-offs in terms of how carefully respondents engage with the survey.

Following the 2020 general election, Republican elected officials, including then-President Donald Trump, promoted conspiracy theories claiming that Joe Biden’s close victory in Georgia was fraudulent. Such conspiratorial claims could implicate participation in the Georgia Senate runoff election in different ways—signaling that voting doesn’t matter, distracting from ongoing campaigns, stoking political anger at out-partisans, or providing rationalizations for (lack of) enthusiasm for voting during a transfer of power. Here, we evaluate the possibility of any on-average relationship with turnout by combining behavioral measures of engagement with election conspiracies online and administrative data on voter turnout for 40,000 Twitter users registered to vote in Georgia. We find small, limited associations. Liking or sharing messages opposed to conspiracy theories was associated with higher turnout than expected in the runoff election, and those who liked or shared tweets promoting fraud-related conspiracy theories were slightly less likely to vote.