[论文解读] Hate is not Binary: Studying Abusive Behavior of #GamerGate on Twitter

Despoina Chatzakou, Nicolas Kourtellis+4 more|arXiv (Cornell University)|May 9, 2017

Hate Speech and Cyberbullying Detection被引用 22

一句话总结

本研究分析了#GamerGate推特争议中的虐待行为，发现尽管内容具有攻击性，Gamergaters表现出的快乐情绪却低于普通用户。通过无监督聚类与有监督分类方法，作者识别出可预测账号被封禁的行为特征，实现了高精确率与高召回率，并揭示了为何具有破坏性行为的用户仍能逃避检测。

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.

研究动机与目标

• 理解虐待性Gamergaters与普通推特用户之间的行为差异。
• 探究尽管内容具有攻击性，Gamergaters为何在Twitter上被封禁的比例显著偏低。
• 识别出与被封账号行为相似的活跃用户群体，并应考虑将其列入封禁名单。
• 评估有监督模型在模拟Twitter封禁机制方面的性能表现。

提出的方法

• 收集并清洗了以#GamerGate争议为核心的大型推特数据集。
• 对Gamergaters与随机推特用户在行为、情感及网络参与度方面进行了对比分析。
• 应用无监督机器学习方法，检测具有类似封禁行为的活跃用户聚类。
• 利用情感特征与活动相关特征训练有监督分类器，以预测用户状态（活跃、删除、封禁）。
• 在GG数据集与基线数据集上，使用精确率、召回率与ROC-AUC指标评估模型性能。
• 分析账号删除模式及自删账号用户的情感特征。

实验结果

研究问题

• RQ1 Gamergaters的行为模式与普通推特用户有何不同？
• RQ2 尽管发布了虐待性内容，为何Gamergaters被Twitter封禁的可能性更低？
• RQ3 在#GamerGate语境下，哪些特征可区分被封用户与活跃或已删除用户？
• RQ4 无监督聚类能否识别出行为与被封用户相似的活跃用户？
• RQ5 基于行为与情感特征，有监督模型在预测用户封禁状态方面的表现如何？

主要发现

• 尽管发布攻击性与仇恨内容，Gamergaters与普通用户的主要区别在于其表达的快乐情绪更少。
• Gamergaters的参与度高于普通用户，拥有更多好友与粉丝，这导致争议持续时间更长。
• 尽管发布了冒犯性内容，Gamergaters被封禁的比例显著低于随机用户。
• 被封禁的Gamergaters表现出更强的攻击性情绪、更多冒犯性语言，且快乐情绪水平高于被封的随机用户。
• 自删账号的用户表现出最高活动水平，伴有明显压力、恐惧与悲伤迹象，且社交网络小而缺乏支持性。
• 有监督模型在预测封禁状态方面表现优异，GG数据集上的平均F1分数超过0.88，AUC值超过0.88。