PI-Bully: Personalized Cyberbullying Detection with Peer Influence
PI-Bully: Personalized Cyberbullying Detection with Peer Influence
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DOI：
10.24963/ijcai.2019/808
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发表时间：
2019-08
期刊：
影响因子：
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通讯作者：
Lu Cheng;Jundong Li;Yasin N. Silva;Deborah L. Hall;Huan Liu
中科院分区：
文献类型：
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作者：
Lu Cheng;Jundong Li;Yasin N. Silva;Deborah L. Hall;Huan Liu
网络欺凌已成为青少年面临的最紧迫的网络风险之一，并引起了社会的严重关注。近年来，旨在开发原则性学习模型以检测网络欺凌行为的研究激增。这些努力主要集中在建立一个单一的通用分类模型，以区分所有用户之间的欺凌内容与正常（非欺凌）内容。这些模型平等对待用户，忽略了可能有助于准确检测网络欺凌的用户的特殊信息。在本文中，我们提出了一个个性化的网络欺凌检测框架，PI欺负，从心理学的实证研究结果突出的独特特征的受害者和欺负者和同伴的影响，从志同道合的用户作为预测的网络欺凌行为。我们的框架是新颖的，它能够在协作环境中模拟同伴的影响，并为每个用户量身定制网络欺凌预测。在真实世界数据集上进行的广泛实验评估证实了所提出的框架的有效性。
Cyberbullying has become one of the most pressing online risks for adolescents and has raised serious concerns in society. Recent years have witnessed a surge in research aimed at developing principled learning models to detect cyberbullying behaviors. These efforts have primarily focused on building a single generic classification model to differentiate bullying content from normal (non-bullying) content among all users. These models treat users equally and overlook idiosyncratic information about users that might facilitate the accurate detection of cyberbullying. In this paper, we propose a personalized cyberbullying detection framework, PI-Bully, that draws on empirical findings from psychology highlighting unique characteristics of victims and bullies and peer influence from like-minded users as predictors of cyberbullying behaviors. Our framework is novel in its ability to model peer influence in a collaborative environment and tailor cyberbullying prediction for each individual user. Extensive experimental evaluations on real-world datasets corroborate the effectiveness of the proposed framework.