@article{ce84549b2c1844d7b01e62d658674b96,
title = "Trumping hate on Twitter? Online hate speech in the 2016 U.S. Election campaign and its aftermath",
abstract = "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.",
keywords = "Donald Trump, Hate speech, Social media, Text-as-data, Twitter",
author = "Siegel, {Alexandra A.} and Evgenii Nikitin and Pablo Barber{\'a} and Joanna Sterling and Bethany Pullen and Richard Bonneau and Jonathan Nagler and Tucker, {Joshua A.}",
note = "Funding Information: ∗The authors gratefully acknowledge the financial support for the NYU Social Media and Political Participation (SMaPP) lab from the INSPIRE program of the National Science Foundation (Award SES-1248077), the William and Flora Hewlett Foundation, the Rita Allen Foundation, the Knight Foundation, the Bill and Melinda Gates Foundation, Craig Newmark Philanthropies, the Democracy Fund, the Intel Corporation, the New York University Global Institute for Advanced Study, and the Faculty of Arts and Sciences Research Investment Fund at New York University. We thank Sean Kates for his feedback in designing our coding scheme, NYU Undergraduate SMaPP Research Assistants for their coding work, and Yvan Scher and Leon Yin for programming support. A.S. and J.T. designed the research plan and outline for the paper. A.S. conducted the statistical analysis and wrote the first draft of the paper. A.S, J.S., and B.P. designed and implemented the dictionary-based coding method. E.N. designed and conducted the non-dictionary based analysis. P.B., R.B., and J.N. contributed to the data collection and design of the analytic tools, and strategy for data analysis and presentation. AS, JT, PB, RB, and JN contributed to revising the manuscript. Publisher Copyright: {\textcopyright} 2021 A. A. Siegel et al.",
year = "2021",
doi = "10.1561/100.00019045",
language = "English (US)",
volume = "16",
pages = "71--104",
journal = "Quarterly Journal of Political Science",
issn = "1554-0626",
publisher = "Now Publishers Inc",
number = "1",
}