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A Survey About the Cyberbullying Problem on Social Media by Using Machine Learning Approaches

机译：利用机器学习方法对社交媒体的百元化问题的调查

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The exponential growth of connected devices (i.e. laptops, smartphones or tablets) has radically changed communications means, also making it faster and impersonal by using On-line Social Networks and Instant messaging through several apps. In this paper we discuss about the cyberbullying problem, focusing on the analysis of the state-of-the-art approaches that can be classified in four different tasks (Binary Classification, Role Identification, Severity Score Computation and Incident prediction). In particular, the first task aims to predict if a particular action is aggressive or not based on the analysis of different features. In turn, the second and the third task investigate the cyberbullying problem by identifying users' role in the exchanged message or assigning a severity score to a given users or session respectively. Nevertheless, information heterogeneity, due to different multimedia contents (i.e. text, emojis, stickers or gifs), and the use of datasets, which are typically unlabeled or manually labelled, create continuous challenges in addressing the cyberbullying problem.
机译：连接设备（即笔记本电脑，智能手机或平板电脑）的指数增长具有彻底改变的通信方式，也通过使用若干应用程序使用在线社交网络和即时消息来实现更快和不同意。在本文中，我们讨论了网络欺凌问题，专注于分析可以分类为四种不同任务的最新方法（二进制分类，角色识别，严重程度计算和事件预测）。特别是，第一任务旨在预测特定动作是否是基于对不同特征的分析。反过来，第二个和第三任务通过识别交换消息中的用户角色或分别为给定用户或会话分配严重性分数来调查克兰布格地区。然而，由于多媒体内容（即文本，表情象征，贴纸或GIF）以及通常未标记或手动标记的数据集，在寻址网络欺凌问题时产生不断挑战的信息异质性。

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