Modeling Temporal Patterns of Cyberbullying Detection with Hierarchical Attention Networks
Modeling Temporal Patterns of Cyberbullying Detection with Hierarchical Attention Networks
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DOI：
10.1145/3441141
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发表时间：
2021-04
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影响因子：
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通讯作者：
Lu Cheng;Ruocheng Guo;Yasin N. Silva;Deborah L. Hall;Huan Liu
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作者：
Lu Cheng;Ruocheng Guo;Yasin N. Silva;Deborah L. Hall;Huan Liu
网络欺凌正在迅速成为青少年面临的最严重的网络风险之一。这推动了机器学习方法的研究，以实现网络欺凌检测过程的自动化，迄今为止，大多数人将网络欺凌视为在单个时间点发生的一次性事件。人们对网络欺凌行为如何发生和随时间演变的了解相对较少。鉴于网络欺凌通常被定义为通过电子通信反复、持续发生的故意侵略行为，这一监督凸显了网络欺凌相关研究面临的一个重要的公开挑战。在本文中，我们集中讨论对网络欺凌行为的时间模式进行建模的挑战。具体来说，我们研究了如何利用社交媒体会话中的时间信息来促进网络欺凌检测，该会话具有固有的层次结构（例如，单词形成评论，评论形成会话）。跨学科研究的最新发现表明，欺凌会话的时间特征与非欺凌会话的时间特征不同，并且用户评论中的时间信息可以改善网络欺凌检测。所提出的框架包含三个显着特征：（1）层次结构，反映社交媒体会话如何以自下而上的方式形成； (2) 在文字和评论级别应用注意机制，以区分文字和评论对社交媒体会话表现的贡献； (3) 在评论级别的网络欺凌行为建模中纳入时间特征。对从 Instagram 收集的真实世界数据集进行定量和定性评估，Instagram 是报告网络欺凌经历的用户比例最高的社交网站。实证评估的结果显示了所提出的方法的重要性，这些方法是为捕获网络欺凌检测的时间模式而定制的。
Cyberbullying is rapidly becoming one of the most serious online risks for adolescents. This has motivated work on machine learning methods to automate the process of cyberbullying detection, which have so far mostly viewed cyberbullying as one-off incidents that occur at a single point in time. Comparatively less is known about how cyberbullying behavior occurs and evolves over time. This oversight highlights a crucial open challenge for cyberbullying-related research, given that cyberbullying is typically defined as intentional acts of aggression via electronic communication that occur repeatedly and persistently. In this article, we center our discussion on the challenge of modeling temporal patterns of cyberbullying behavior. Specifically, we investigate how temporal information within a social media session, which has an inherently hierarchical structure (e.g., words form a comment and comments form a session), can be leveraged to facilitate cyberbullying detection. Recent findings from interdisciplinary research suggest that the temporal characteristics of bullying sessions differ from those of non-bullying sessions and that the temporal information from users’ comments can improve cyberbullying detection. The proposed framework consists of three distinctive features: (1) a hierarchical structure that reflects how a social media session is formed in a bottom-up manner; (2) attention mechanisms applied at the word- and comment-level to differentiate the contributions of words and comments to the representation of a social media session; and (3) the incorporation of temporal features in modeling cyberbullying behavior at the comment-level. Quantitative and qualitative evaluations are conducted on a real-world dataset collected from Instagram, the social networking site with the highest percentage of users reporting cyberbullying experiences. Results from empirical evaluations show the significance of the proposed methods, which are tailored to capture temporal patterns of cyberbullying detection.