Hate is not Binary: Studying Abusive Behavior of #GamerGate on Twitter

TL;DR

This study analyzes abusive behavior on Twitter during the Gamergate controversy, revealing unique user characteristics, behavioral patterns, and potential indicators for suspension using machine learning techniques.

Contribution

It provides a detailed analysis of Gamergate-related users, identifying features that distinguish them from typical users and predicting suspension likelihood with supervised learning.

Findings

Gamergaters are less joyful and more engaged than typical users.

They are less likely to be suspended despite aggressive content.

Unsupervised clustering reveals user groups with suspension risk.

Abstract

Over the past few years, online bullying and aggression have become increasingly prominent, and manifested in many different forms on social media. However, there is little work analyzing the characteristics of abusive users and what distinguishes them from typical social media users. In this paper, we start addressing this gap by analyzing tweets containing a great large amount of abusiveness. We focus on a Twitter dataset revolving around the Gamergate controversy, which led to many incidents of cyberbullying and cyberaggression on various gaming and social media platforms. We study the properties of the users tweeting about Gamergate, the content they post, and the differences in their behavior compared to typical Twitter users. We find that while their tweets are often seemingly about aggressive and hateful subjects, "Gamergaters" do not exhibit common expressions of online anger, and in fact primarily differ from typical users in that their tweets are less joyful. They are also more engaged than typical Twitter users, which is an indication as to how and why this controversy is still ongoing. Surprisingly, we find that Gamergaters are less likely to be suspended by Twitter, thus we analyze their properties to identify differences from typical users and what may have led to their suspension. We perform an unsupervised machine learning analysis to detect clusters of users who, though currently active, could be considered for suspension since they exhibit similar behaviors with suspended users. Finally, we confirm the usefulness of our analyzed features by emulating the Twitter suspension mechanism with a supervised learning method, achieving very good precision and recall.