Big Five Personality Traits and Ensemble Machine Learning to Detect Cyber-Violence in Social Media
Big Five Personality Traits and Ensemble Machine Learning to Detect Cyber-Violence in Social Media
复制标题
大五人格特征和集成机器学习检测社交媒体中的网络暴力
DOI：
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
Mounia Abik
中科院分区：
文献类型：
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作者：
Randa Zarnoufi;Mounia Abik
网络暴力是电子健康研究中的一个主要问题，其重点是从在线用户生成的内容中检测有害行为，以预防和保护受害者。在这项工作中，我们展示了五大人格特质与网络暴力犯罪者的暴力行为之间的关系。我们使用了一套集成学习算法，这些算法具有与每个大五人格特质中使用的词汇相关的工程特征，即可接受性，尽责性，外向性，神经质和开放性。研究结果显示，个体的人格状态与伤害意图之间存在显著的关联。这一结果可以很好地表明在线用户对网络暴力的敏感性，因此可以帮助应对网络暴力。
Cyber-violence is a largely addressed problem in e-health researches, its focus is the detection of harmful behavior from online user-generated content in order to prevent and protect victims. In this work, we show how big five personality traits are correlated to the violent behavior of the cyber-violence perpetrator. We use a set of ensemble learning algorithms with engineered features related to the vocabulary used in each Big Five personality trait namely, Agreeableness, Conscientiousness, Extraversion, Neuroticism and Openness. The findings show a significant association between the individuals’ personality state and the harmful intention. This result can be a good indicator of online users’ susceptibility to cyber-violence and therefore can help in dealing with it.