Trumping Hate on Twitter? Online Hate Speech in the 2016 US Election Campaign and its Aftermath

Speaker Series on Misinformation, co-sponsored by the NULab at Northeastern University.

As the role of social media platforms in fostering extremism and offline violence has come under scrutiny, online hate speech has received increased attention from academics and policy makers alike. But despite a growing body of research devoted to defining and detecting online hate speech, the existing scientific literature lacks a systematic framework for assessing how the volume and content of these harmful messages change over time. Offering a new approach to measuring the real-time dynamics of online hate, this paper explores whether and to what extent hate speech and white nationalist rhetoric on Twitter increased over the course of Donald Trump’s 2016 presidential campaign and in the aftermath of his election. The prevailing narrative suggests that Trump’s political rise—and his unexpected victory—led to a “mainstreaming” of online bigoted rhetoric that was once relegated to the dark corners of the Internet. However, our analysis of over 750 million tweets related to the election, in addition to almost 400 million tweets from a random sample of American Twitter, over a two year period, provides novel evidence that runs counter to this narrative. Using both machine-learning-augmented dictionary-based methods and an original classification approach leveraging data from Reddit communities associated with the alt-right movement, we observe no persistent increase in hate speech or white nationalist language either over the course of the campaign or in the aftermath of Trump’s election. Instead, hate speech was “bursty”: while there were notable spikes in hateful language, these effects quickly dissipated. Demonstrating the importance of studying online behavior systematically over time, we find no empirical support for the proposition that the Trump phenomenon systematically mainstreamed online hate on Twitter.

Joshua A. Tucker is Professor of Politics, an affiliated Professor of Russian and Slavic Studies, and an affiliated Professor of Data Science at New York University. He is the Director of NYU’s Jordan Center for Advanced Study of Russia. He is one of the co-founders and co-Directors of the NYU Social Media and Political Participation (SMaPP) laboratory, and the Director of SMaPP-Global, an international collection of scholars working on the study of social media and politics funded by the NYU Global Institute of Advanced Study.

Professor Tucker specializes in comparative politics with an emphasis on mass political behavior in East-Central Europe and the former Soviet Union, including elections and voting, the development of partisan attachment, public opinion formation, and mass protest, as well as the use of social media in facilitating all forms of political participation. He is the author of Regional Economic Voting: Russia, Poland, Hungary, Slovakia, and the Czech Republic, 1990-99 (Cambridge University Press, 2006), and co-author of the forthcoming Communism’s Shadow: Historical Legacies and Contemporary Political Attitudes (Princeton University Press, 2017). His work has appeared in numerous academic journals, including the American Journal of Political Science, the British Journal of Political Science, Comparative Politics, Electoral Studies, Comparative Political Studies, the Journal of Politics, Political Analysis, Political Science and Research Methods, PLOS One, Psychological Science, Social Media and Society, and the Annual Review of Political Science.