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DATA POLITICS

Blå farve i vand - photo: colourboxThe Centre for Social Data Science is pleased to announce its Fall Lecture Series 2018. Focusing on the theme of “Data Politics”, SODAS has invited speakers to reflect on the role of politics in the age of (big) data. Speaking on themes such as political elections, fake news, data privacy and the globalization of data, the lectures will present cutting edge research on the political challenges the digital revolution presents us with, and the methodological innovations needed to study them.


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

Our final speaker of the fall 2018 is Joshua Tucker, who is Professor of Politics, Director Jordan Center for the Advanced Study of Russia, Co-Director NYU Social Media and Political Participation (SMaPP) lab.

Abstract

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.

The lecture will take place in building 1, 1st floor, room 18 (1.1.18) of the CSS Campus, Copenhagen University, from 11.00am - 12.30pm.

If you have questions or want to know more, please write Agnete Vienberg Hansen at avh@econ.ku.dk.