Publications

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.

With a dataset of testing and case counts from over 1,400 institutions of higher education (IHEs) in the United States, we analyze the number of infections and deaths from SARS-CoV-2 in the counties surrounding these IHEs during the Fall 2020 semester (August to December, 2020). We find that counties with IHEs that remained primarily online experienced fewer cases and deaths during the Fall 2020 semester; whereas before and after the semester, these two groups had almost identical COVID-19 incidence. Additionally, we see fewer cases and deaths in counties with IHEs that reported conducting any on-campus testing compared to those that reported none. To perform these two comparisons, we used a matching procedure designed to create well-balanced groups of counties that are aligned as much as possible along age, race, income, population, and urban/rural categories—demographic variables that have been shown to be correlated with COVID-19 outcomes. We conclude with a case study of IHEs in Massachusetts—a state with especially high detail in our dataset—which further highlights the importance of IHE-affiliated testing for the broader community. The results in this work suggest that campus testing can itself be thought of as a mitigation policy and that allocating additional resources to IHEs to support efforts to regularly test students and staff would be beneficial to mitigating the spread of COVID-19 in a pre-vaccine environment.

The public often turns to science for accurate health information, which, in an ideal world, would be error free. However, limitations of scientific institutions and scientific processes can sometimes amplify misinformation and disinformation. The current review examines four mechanisms through which this occurs: (1) predatory journals that accept publications for monetary gain but do not engage in rigorous peer review; (2) pseudoscientists who provide scientific-sounding information but whose advice is inaccurate, unfalsifiable, or inconsistent with the scientific method; (3) occasions when legitimate scientists spread misinformation or disinformation; and (4) miscommunication of science by the media and other communicators. We characterize this article as a “call to arms,” given the urgent need for the scientific information ecosystem to improve. Improvements are necessary to maintain the public’s trust in science, foster robust discourse, and encourage a well-educated citizenry.

Scholars have long documented unequal access to the benefits of science among different groups in the United States. Particular populations, such as low-income, non–white people, and Indigenous people, fare worse when it comes to health care, infectious diseases, climate change, and access to technology. These types of inequities can be partially addressed with targeted interventions aimed at facilitating access to scientific information. Doing so requires knowledge about what different groups think when it comes to relevant scientific topics. Yet data collection efforts for the study of most science-based issues do not include enough respondents from these populations. We discuss this gap and offer an overview of pertinent sampling and administrative considerations in studying underserved populations. A sustained effort to study diverse populations, including through community partnerships, can help to address extant inequities.

Research on complex contagions suggests that individuals need social reinforcement from multiple sources before they are convinced to adopt costly behaviors. We tested the causal foundation of complex contagions in a country-scale viral marketing field experiment. The experiment used a peer encouragement design in which a randomly sampled set of customers were encouraged to share a coupon for a mobile data product with their friends. This experimental design allowed us to test the causal effects of neighboring adopters on the product adoption of their own neighbors. We find causal evidence of complex contagions in viral marketing: contact with one neighboring adopter increases product adoption 3.5-fold, while contact with a second neighbor increases it 4-fold. We also find that social reinforcement crucially depends on the local network structure that supports or constrains peer influences: the more friends two individuals have in common—the more embedded their relationship is—the stronger the effect. While the effect of social reinforcement is quite large, we show that the ability to generate such reinforcement in a realistic setting may be limited when the marketer cannot directly control the messages that customers send to their friends.

Popular online platforms such as Google Search have the capacity to expose billions of users to partisan and unreliable news. Yet, the content they show real users is understudied due to the technical challenges of independently obtaining such data, and the lack of data sharing agreements that include it. Here we advance on existing digital trace methods using a two-wave study in which we captured not only the URLs participants clicked on while browsing the web (engagement), but also the URLs they saw while using Google Search (exposure). Using surveys paired with engagement and exposure data collected around the 2018 and 2020 US elections, we found that strong Republicans engaged with more partisan and unreliable news than strong Democrats did, despite the two groups being exposed to similar amounts of partisan and unreliable news in their Google search results. Our results suggest the search engine is not pushing strong partisans into filter bubbles, but strong Republicans are asymmetrically selecting into echo chambers. These findings hold across both study waves, align with work on social media and web browsing, and provide a rare look at the relationship between exposure and engagement. Our research highlights the importance of users' choices, and our approach moves the field closer to the independent, longitudinal, and cross-platform studies it needs to evaluate the impact of online search and social media platforms.