Trumping hate on Twitter? Online hate speech in the 2016 U.S. Election campaign and its aftermath

Alexandra A. Siegel, Evgenii Nikitin, Pablo Barberá, Joanna Sterling, Bethany Pullen, Richard Bonneau, Jonathan Nagler, Joshua A. Tucker

Research output: Contribution to journalArticlepeer-review


To what extent did online hate speech and white nationalist rhetoric on Twitter increase over the course of Donald Trump's 2016 presidential election campaign and its immediate aftermath? The prevailing narrative suggests that Trump's political rise - and his unexpected victory - lent legitimacy to and popularized 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 users, provides systematic evidence that hate speech did not increase on Twitter over this period. Using both machine-learning-augmented dictionary-based methods and a novel 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 six months following Trump's election. While key campaign events and policy announcements produced brief spikes in hateful language, these bursts quickly dissipated. Overall we find no empirical support for the proposition that Trump's divisive campaign or election increased hate speech on Twitter.

Original languageEnglish (US)
Pages (from-to)71-104
Number of pages34
JournalQuarterly Journal of Political Science
Issue number1
StatePublished - 2021


  • Donald Trump
  • Hate speech
  • Social media
  • Text-as-data
  • Twitter

ASJC Scopus subject areas

  • Sociology and Political Science
  • Political Science and International Relations


Dive into the research topics of 'Trumping hate on Twitter? Online hate speech in the 2016 U.S. Election campaign and its aftermath'. Together they form a unique fingerprint.

Cite this