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Combining textual features to detect cyberbullying in social media posts

机译:组合文本功能以检测社交媒体帖子中的网络欺凌

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Cyberbullying has become prevalent in social media communication. To create a safe space for cyber communication, an effective cyberbullying detection method is needed. This study focuses on using combination of textual features to detect cyberbullying across social media platforms. Lexicon enhanced rule-based method was applied to detect cyberbullying on Facebook comments. The resulting algorithm was evaluated using performance measures of accuracy, precision, recall, and F1 Score, and showed promising performance with average recall of 95.981%.
机译:网络欺凌在社交媒体沟通中已经普遍存在。为了为网络通信创建安全空间,需要一种有效的网络欺负检测方法。本研究侧重于使用文本功能的组合来检测社交媒体平台的网络欺凌。 Lexicon增强的基于规则的方法被应用于在Facebook评论中检测网络欺凌。通过精度,精度,召回和F1分数的性能测量评估所得算法,并显示出具有95.981%的平均召回的有前途的性能。

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