论文标题
社交媒体中基于会话的网络欺凌检测：一项调查
Session-based Cyberbullying Detection in Social Media: A Survey
论文作者
论文摘要
网络欺凌是在线社交媒体中普遍存在的问题，在线社交媒体上，欺负者通过社交媒体会议虐待受害者。通过调查通过社交媒体会议进行的网络欺凌行为，最近的研究研究了采矿模式和特征，以建模和理解网络欺凌的两个定义特征：重复行为和权力不平衡。在本调查文件中，我们定义了基于会话的网络欺凌检测框架，该检测框架封装了问题的不同步骤和挑战。基于此框架，我们对社交媒体中基于会话的网络欺凌检测进行了全面概述，从数据和方法论角度研究了现有的努力。我们的评论使我们提出了一组最佳实践的循证标准，以创建基于会话的网络欺凌数据集。此外，我们执行基准实验，以比较基于最新会话的网络欺凌检测模型的性能以及两个不同数据集的大型预训练的语言模型。通过我们的审查，我们还提出了一系列公开挑战作为未来的研究方向。
Cyberbullying is a pervasive problem in online social media, where a bully abuses a victim through a social media session. By investigating cyberbullying perpetrated through social media sessions, recent research has looked into mining patterns and features for modeling and understanding the two defining characteristics of cyberbullying: repetitive behavior and power imbalance. In this survey paper, we define the Session-based Cyberbullying Detection framework that encapsulates the different steps and challenges of the problem. Based on this framework, we provide a comprehensive overview of session-based cyberbullying detection in social media, delving into existing efforts from a data and methodological perspective. Our review leads us to propose evidence-based criteria for a set of best practices to create session-based cyberbullying datasets. In addition, we perform benchmark experiments comparing the performance of state-of-the-art session-based cyberbullying detection models as well as large pre-trained language models across two different datasets. Through our review, we also put forth a set of open challenges as future research directions.
