[论文解读] Racism is a Virus: Anti-Asian Hate and Counterhate in Social Media during the COVID-19 Crisis

Caleb Ziems, Bing He+2 more|arXiv (Cornell University)|May 25, 2020

Hate Speech and Cyberbullying Detection参考文献 78被引用 83

一句话总结

本研究利用包含3000多万条推文和8700多万个节点的COVID-HATE数据集，分析了新冠疫情期间推特平台上的反亚裔仇恨言论及其反制言论。研究发现仇恨具有传染性，机器人账号加剧了仇恨言论的传播，而反制言论能有效降低用户转向仇恨言论的可能性，为危机期间应对网络种族主义提供了数据驱动的解决方案。

ABSTRACT

The spread of COVID-19 has sparked racism, hate, and xenophobia in social media targeted at Chinese and broader Asian communities. However, little is known about how racial hate spreads during a pandemic and the role of counterhate speech in mitigating the spread. Here we study the evolution and spread of anti-Asian hate speech through the lens of Twitter. We create COVID-HATE, the largest dataset of anti-Asian hate and counterhate spanning three months, containing over 30 million tweets, and a social network with over 87 million nodes. By creating a novel hand-labeled dataset of 2,400 tweets, we train a text classifier to identify hate and counterhate tweets that achieves an average AUROC of 0.852. We identify 891,204 hate and 200,198 counterhate tweets in COVID-HATE. Using this data to conduct longitudinal analysis, we find that while hateful users are less engaged in the COVID-19 discussions prior to their first anti-Asian tweet, they become more vocal and engaged afterwards compared to counterhate users. We find that bots comprise 10.4% of hateful users and are more vocal and hateful compared to non-bot users. Comparing bot accounts, we show that hateful bots are more successful in attracting followers compared to counterhate bots. Analysis of the social network reveals that hateful and counterhate users interact and engage extensively with one another, instead of living in isolated polarized communities. Furthermore, we find that hate is contagious and nodes are highly likely to become hateful after being exposed to hateful content. Importantly, our analysis reveals that counterhate messages can discourage users from turning hateful in the first place. Overall, this work presents a comprehensive overview of anti-Asian hate and counterhate content during a pandemic. The COVID-HATE dataset is available at this http URL.

研究动机与目标

• 理解新冠疫情期间社交媒体上反亚裔仇恨言论及其反制言论的动态机制。
• 研究仇恨言论用户与反制仇恨言论用户在参与度、传播性及网络行为方面的差异。
• 考察机器人账号在放大仇恨与反制仇恨内容方面所起的作用。
• 评估用户接触反制仇恨言论是否能降低其采取仇恨行为的可能性。
• 创建并发布一个大规模、人工标注的数据集（COVID-HATE），以供未来研究公共卫生危机期间的网络仇恨言论。

提出的方法

• 构建了COVID-HATE数据集，涵盖疫情期间三个月内来自推特的3000多万条推文和8700多万个节点。
• 创建了一个包含2400条推文的人工标注数据集，用于训练文本分类器以检测仇恨与反制仇恨言论，平均AUROC达到0.852。
• 采用纵向分析方法，比较用户在首次发布仇恨或反制仇恨推文前后的行为变化与参与度差异。
• 识别并分类仇恨与反制仇恨用户中的机器人账号，比较其传播性与粉丝增长情况。
• 绘制仇恨与反制仇恨用户的社会网络图，分析互动模式与内容传播机制。
• 利用人工标注数据进行监督学习，训练并评估文本分类模型，实现在大规模上检测仇恨与反制仇恨内容。

实验结果

研究问题

• RQ1 仇恨言论用户与反制仇恨言论用户在发布仇恨内容前后，其参与度与传播性模式有何差异？
• RQ2 机器人账号在多大程度上促进了推特上反亚裔仇恨与反制仇恨言论的传播？
• RQ3 仇恨言论用户与反制仇恨言论用户是否存在于孤立的极化社群中，还是存在跨意识形态的互动？
• RQ4 接触反制仇恨内容是否与用户减少采取仇恨行为的可能性相关？
• RQ5 仇恨在在线社交网络中传播的传染性有多强？网络结构在其中起到何种作用？

主要发现

• 与反制仇恨言论用户相比，仇恨言论用户在首次发布反亚裔仇恨推文后，其在推特上的参与度和活跃度显著提高。
• 仇恨用户中10.4%为机器人账号，其言论更具攻击性且传播更广泛，且仇恨类机器人吸引的粉丝数量多于反制仇恨类机器人。
• 仇恨言论用户与反制仇恨言论用户频繁互动，表明二者并非存在于孤立的极化社群中。
• 接触仇恨内容会增加用户采取仇恨行为的可能性，证明仇恨在在线网络中具有传染性。
• 反制仇恨言论能有效降低用户转向仇恨行为的可能性，表明其对极端化具有保护作用。
• 本研究在COVID-HATE数据集中识别出891,204条仇恨推文和200,198条反制仇恨推文，为未来研究提供了丰富的资源。