I'm an Assistant Professor of Sociology at Rutgers University—New Brunswick. My research and teaching interests include political sociology and social movements, culture, and social networks. I specialize in using computational methods including natural language processing and machine-learning to analyze data collected from social media platforms. My work has been published in venues including Social Forces, Mobilization, and Socius.
My main area of research builds upon the fact that social media technologies have both transformed politics and our ability to study it. My current research projects address several related questions. Why have far-right, populist actors have been so successful at building remarkably large online audiences? Has this disproportionate online presence contributed to their recent electoral breakthroughs? To what extent is online social movement activity, engagement, and recruitment associated with offline events like protests, elections, and terrorist attacks? And what role do individual opinion leaders play in disseminating information and influencing public opinion in massive online debates? To address these questions, I draw upon theories from political sociology, social movements, and public opinion and examine data collected from social media, newspapers, and other sources.
In a paper published in Mobilization, Mabel Berezin and I examine the relationship between Britain First and the UK Independence Party. Drawing upon newspaper data, press releases, and social media data, we study the relationship dynamics at both the elite and grassroots levels. While UKIP publicly disavowed the extremist movement, data from Facebook reveal extensive connections between their grassroots supporters, providing a window into the connections between radical right movements and parties.
Most of my work focuses on the United Kingdom, but I am also studying these trends more broadly. I have published analyses in the Washington Post on the 2017 German election and the 2018 Italian election, in both cases radical right breakthroughs were associated with dominance on social media. While these case studies are suggestive, it is possible that the observed correlation between social media and electoral success may be attributable to other factors. To more systematically examine this relationship, I am currently conducting a comparative analysis using over a decade of data from over thirty European countries, using statistical modeling to account for a variety of explanatory variables.
A second area of my research focuses on identifying and understanding hate speech and hate speakers on social media. Our initial work on the subject focused on the distinction between hate speech and other forms of offensive language that often result in false positives here. This research was been covered in Wired Magazine, Tech Republic, and New Scientist. This led to further collaborations focused on understanding different dimensions of hateful and abusive language paper focusing on the commonalities between hate speech detection and other topics, including online abuse and cyberbullying, for the Association for Computational Linguistics 1st Workshop of Abusive Language Online. Recently, I have examined the potential for racial bias in hate speech and abusive language detection detection systems, demonstrating how machine-learning classifiers designed to detect hate speech tend to predict that tweets written in African-American English are hateful than similar tweets written in Standard American English. You can read the paper, along with coverage in Vox.
I collaborated with Paromita Sanyal to study the impact of microcredit participation on women's social networks in rural India. We found that participation in associations allows women to expand their personal networks and enhance their social capital. Our work was published in Social Forces. I am currently working on a project with my colleague Antonio Sirianni to study the career trajectories of adult film performers and another solo-authored project to study how co-performance and co-production shape the development of musical genres.
In addition to my substantive interests, I also study how computational methods can be applied more generally in sociological research. For example, as part of the Fragile Families Challenge, I examined how neural networks can enable us to predict social outcomes and assessed the extent to which these black box predictive models can be amenable to sociological explanations. I found that these methods do not radically outperform traditional approaches like linear regression, but may allow us to use large amounts of data to inductively identify important variables. A paper based on my analysis is published Socius and our paper describing the results of the Fragile Families Challenge mass collaboration was published in PNAS.
In addition to my academic research, I have experience using computational methods in industry settings. In the summer of 2016 I was an Eric and Wendy Schmidt Data Science for Social Good Fellow at the University of Chicago. I worked on a project to develop an early-warning system to identify police misconduct and helped develop a new model to predict risks at the dispatch level. You can read about our work here, along with media coverage in The Chicago Tribune, NPR, Mother Jones, the Economist, and Forbes. As of spring 2017 our system is being implemented into two police departments, you can read about the progress here. In 2017 I spent the summer in the Data Science Research & Development group at Civis Analytics, a data science consulting and software company based in Chicago. I used natural language processing and machine learning techniques to build a tool to monitor political discussion on Twitter. In 2018 I was a Core Data Science intern at Facebook in Menlo Park. I conducted a study of misinformation sharing on the platform and helped to evaluate and deploy a new tool related to their on-going election integrity efforts.
Please feel free to get in touch at thomas dot davidson at rutgers dot edu.