My work examines the ways in which digital platforms and social networks transform how we communicate, how we seek and evaluate (mis)information, how we get mobilized for action or discouraged from participating in politics. I study how our social interactions, both in person and online, influence what we know, what we believe to be true, and how we act upon it. To investigate patterns of direct and mediated social influence, I use a network science approach and rely on a mix of digital trace and self-reported data. My past work has examined changes in the the media system, political and civic participation, and the social aspects of new technologies. My agenda as a scholar also includes advancing computational and network methods, as well as promoting ethical research practices, transparency, and replicability in the social sciences.

While I use a variety of tools* for data manipulation and statistical analysis, the bulk of my work is done using the R language for statistical computing + RStudio. You can find my R tips & tutorials on my blog or my GitHub page.

My recent papers and publications are available here.

Current projects

My current work includes projects that examine the impact of social structures and technology on civic behavior and spreading misinformation; a theory-building endeavor extending our understanding of communication and influence flows; and a project on network methodology. More recently, I have also been studying the social disruptions caused by the COVID-19 pandemic, as well as the flows of information about it.

The COVID States Project: This multi-university NSF-funded project was launched in collaboration with David Lazer at Northeastern University and Matthew Baum at Harvard University. It includes a large-scale longitudinal data collection that aims to provide timely information to communities and policymakers. Monthly surveys in all 50 states are combined with social media data from a panel of consenting survey respondents. Network-based survey questions allow us to assess the spread of COVID-19 and its impact not only on our participants, but also on their personal networks and local communities. The study aims to track the prevalence of the virus, as well as its social, technological, and economic consequences. We examine how well the information and communication needs of Americans are met during this crisis. Importantly, the project also seeks to understanding the impact of the pandemic on communities of color which are disproportionately affected by this crisis. Reports, data, and interactive charts from the project are available on

Social networks and civic behavior: This longitudinal multi-site research project was developed with support from Rutgers Student Affairs Research and Assessment, and the Department of Residence Life. The research examines the formation, structure, and impact of social connections among members of fourteen living-learning communities (LLCs) at Rutgers University. It compares the online and offline social ties in each community and examines their influence on individual behavior in the areas of civic and political engagement, information seeking, health, and academic performance. My articles based on this project have examined the influence of social networks on trust in institutions, as well as on political knowledge.

Social influence in misinformation processing: The proliferation of misinformation is both a social and an academic problem. My interest in it stems from a research agenda positioned at the intersection of interpersonal networks and technology. My work in that domain explores how individual traits and news consumption practices interact with influence from our social contacts to affect our evaluation of online information. Several of my current projects explore those issues. One study in that area involves a series of online experiments examining how social factors affect people’s intention to share potentially false news content on Facebook. Another project is a collaboration with computational social science scholars at Northeastern University’s Network Science Institute, using a methodologically innovative approach that combines self-reported data with detailed records records of online behavior. One article based on this project (accepted for publication in Harvard Kennedy School’s Misinformation Review journal) finds a link between consuming misinformation and trust in media & political institutions.

* Beyond R, other statistical software packages I have experience with include SPSS, Stata, LISREL & PRELIS, and AMOS. The network analysis & visualization tools I’ve worked with include igraph, Statnet, RSiena, the PNet family, Gephi, NodeXL, Pajek, and UCINET.