The potentially detrimental effects of cyberbullying have led to the development of numerous automated, data-driven approaches, with emphasis on classification accuracy. Cyberbullying, as a form of abusive online behavior, although not well-defined, is a repetitive process, i.e., a sequence of aggressive messages sent from a bully to a victim over a period of time with the intent to harm the victim. Existing work has focused on harassment (i.e., using profanity to classify toxic comments independently) as an indicator of cyberbullying, disregarding the repetitive nature of this harassing process. However, raising a cyberbullying alert immediately after an aggressive comment is detected can lead to a high number of false positives. At the same time, two key practical challenges remain unaddressed: (i) detection timeliness, which is necessary to support victims as early as possible, and (ii) scalability to the staggering rates at which content is generated in online social networks. In this work, we introduce CONcISE , a novel approach for timely and accurate Cyberbullying detectiON in online social media SEssions. CONcISE is a two-stage online approach designed to reduce the time to raise a cyberbullying alert by sequentially examining comments as they become available over time, and minimizing the number of feature evaluations necessary for a decision to be made for each comment. Extensive experiments on a real-world Instagram dataset with users and comments demonstrate the effectiveness, scalability, and timeliness of our approach and its benefits over existing methods. Additional experiments using a Twitter dataset offer evidence in support of the potential generalizability of CONcISE to other social media platforms.

中文翻译：

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在线社交媒体中网络欺凌的动态、增量和持续检测

网络欺凌的潜在有害影响导致开发了许多自动化的、数据驱动的方法，重点是分类准确性。网络欺凌虽然没有明确定义，但作为一种滥用在线行为的形式，是一个重复的过程，即在一段时间内从欺凌者发送给受害者的一系列攻击性消息，目的是伤害受害者。现有工作侧重于骚扰（即使用亵渎来独立分类有毒评论）作为网络欺凌的指标，而忽略了这种骚扰过程的重复性。但是，在检测到攻击性评论后立即发出网络欺凌警报可能会导致大量误报。同时，两个关键的实际挑战仍未解决：（i）检测及时性，这对于尽早支持受害者是必要的，以及 (ii) 可扩展至在线社交网络中生成内容的惊人速率。在这项工作中，我们介绍简洁的，一种在在线社交媒体会话中及时准确地检测网络欺凌的新方法。简洁的是一种两阶段的在线方法，旨在通过随着时间的推移依次检查评论，并最大限度地减少为每条评论做出决定所需的特征评估次数，从而减少发出网络欺凌警报的时间。在真实世界的 Instagram 数据集上进行广泛的实验 用户和 评论证明了我们的方法的有效性、可扩展性和及时性，以及它相对于现有方法的优势。使用 Twitter 数据集的其他实验提供了支持潜在普遍性的证据简洁的到其他社交媒体平台。