Publications

The ongoing, fluid nature of the COVID-19 pandemic requires individuals to regularly seek information about best health practices, local community spreading, and public health guidelines. In the absence of a unified response to the pandemic in the United States and clear, consistent directives from federal and local officials, people have used social media to collectively crowdsource COVID-19 elites, a small set of trusted COVID-19 information sources. We take a census of COVID-19 crowdsourced elites in the United States who have received sustained attention on Twitter during the pandemic. Using a mixed methods approach with a panel of Twitter users linked to public U.S. voter registration records, we find that journalists, media outlets, and political accounts have been consistently amplified around COVID-19, while epidemiologists, public health officials, and medical professionals make up only a small portion of all COVID-19 elites on Twitter. We show that COVID-19 elites vary considerably across demographic groups, and that there are notable racial, geographic, and political similarities and disparities between various groups and the demographics of their elites. With this variation in mind, we discuss the potential for using the disproportionate online voice of crowdsourced COVID-19 elites to equitably promote timely public health information and mitigate rampant misinformation.

The field of computational social science (CSS) has exploded in prominence over the past decade, with thousands of papers published using observational data, experimental designs, and large-scale simulations that were once unfeasible or unavailable to researchers. These studies have greatly improved our understanding of important phenomena, ranging from social inequality to the spread of infectious diseases. The institutions supporting CSS in the academy have also grown substantially, as evidenced by the proliferation of conferences, workshops, and summer schools across the globe, across disciplines, and across sources of data. But the field has also fallen short in important ways. Many institutional structures around the field—including research ethics, pedagogy, and data infrastructure—are still nascent. We suggest opportunities to address these issues, especially in improving the alignment between the organization of the 20th-century university and the intellectual requirements of the field.

Research at the intersection of machine learning and the social sciences has provided critical new insights into social behavior. At the same time, a variety of issues have been identified with the machine learning models used to analyze social data. These issues range from technical problems with the data used and features constructed, to problematic modeling assumptions, to limited interpretability, to the models' contributions to bias and inequality. Computational researchers have sought out technical solutions to these problems. The primary contribution of the present work is to argue that there is a limit to these technical solutions. At this limit, we must instead turn to social theory. We show how social theory can be used to answer basic methodological and interpretive questions that technical solutions cannot when building machine learning models, and when assessing, comparing, and using those models. In both cases, we draw on related existing critiques, provide examples of how social theory has already been used constructively in existing work, and discuss where other existing work may have benefited from the use of specific social theories. We believe this paper can act as a guide for computer and social scientists alike to navigate the substantive questions involved in applying the tools of machine learning to social data.

One of the most concerning notions for science communicators, fact-checkers, and advocates of truth, is the backfire effect; this is when a correction leads to an individual increasing their belief in the very misconception the correction is aiming to rectify. There is currently a debate in the literature as to whether backfire effects exist at all, as recent studies have failed to find the phenomenon, even under theoretically favorable conditions. In this review, we summarize the current state of the worldview and familiarity backfire effect literatures. We subsequently examine barriers to measuring the backfire phenomenon, discuss approaches to improving measurement and design, and conclude with recommendations for fact-checkers. We suggest that backfire effects are not a robust empirical phenomenon, and more reliable measures, powerful designs, and stronger links between experimental design and theory, could greatly help move the field ahead.

In March 2020, many state and local governments in the United States enacted stay-at-home policies banning mass gatherings, closing schools, and promoting remote working. By analyzing anonymized location data from millions of mobile devices, we quantify how much people have reduced their daily mobility and physical contacts in accordance with these guidelines. At the regional level, we measure declines in daily commute volume as well as transit between major urban areas. At the individual level, we measure changes in the average user's daily range of mobility, number of unique contacts, and number of co-location events. According to these five measures, we estimate that the average person in the United States had reduced their daily mobility by between 45-55% as of late April, 2020, and had reduced their daily contacts between 65-75%. The United States' physical distancing guidelines expired on April 30, 2020 and are not set to be renewed; as of early May, 2020, we report increases in mobility and contact patterns across most states (up to 10-14%, compared to the last week of April), though we do not observe a commensurate increase in commute volume. The response to the COVID-19 pandemic has amounted to one of the largest disruptions of economic, social, and mobility behavior in history, and quantifying these disruptions is vital for forecasting the further spread of this pandemic and crafting our collective response.

On March 16, 2020, the United States government issued new guidelines promoting public health social social distancing interventions to reduce the spread of the COVID-19 epidemic in the country [1]. In addition, many state and local governments in the United States have enacted stay-at-home policies banning mass gatherings, enforcing school closures, and promoting smart working. So far, however, the extent to which these policies have resulted in reduced people's mobility has not been quantified. By analyzing data from millions of (anonymized, aggregated, privacy-enhanced) devices, we estimate that by March 23 the policies have generally reduced by half the overall mobility in several major U.S. cities. In order to gauge the observed results we know events, we note that the commuting volume on Monday, March 16, approached those of a typical snow day or analogous day when public schools are partially closed (i.e. January 2). By Friday, March 20, we observe commuting numbers that resemble those measured on federal holidays (i.e. Martin Luther King Jr. Day in January or Presidents' Day in February). Currently, we are unable to quantify the extent to which this reduced commuting volume is driven by people working from home or simply an increase in unemployment, though it is surely a mixture of both. Whether this reduction in mobility is enough to change the course of this pandemic is not yet known, but it does provide guidance for further measures that can be implemented at a national scale in the United States.

The internet has become a popular resource to learn about health and to investigate one's own health condition. However, given the large amount of inaccurate information online, people can easily become misinformed. Individuals have always obtained information from outside the formal health care system, so how has the internet changed people's engagement with health information? This review explores how individuals interact with health misinformation online, whether it be through search, user-generated content, or mobile apps. We discuss whether personal access to information is helping or hindering health outcomes and how the perceived trustworthiness of the institutions communicating health has changed over time. To conclude, we propose several constructive strategies for improving the online information ecosystem. Misinformation concerning health has particularly severe consequences with regard to people's quality of life and even their risk of mortality; therefore, understanding it within today's modern context is an extremely important task.