【摘 要】
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This paper describes an approach to identify suspected cybermob on social media.Many researches involve making predictions of group emotion on Internet(such
【机 构】
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School of Computer Science and Technology,Beijing Institute of Technology,Beijing 100081,China
【出 处】
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第十五届全国计算语言学学术会议(CCL2016)暨第四届基于自然标注大数据的自然语言处理国际学术研讨会(NLP-NABD
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This paper describes an approach to identify suspected cybermob on social media.Many researches involve making predictions of group emotion on Internet(such as quantifying sentiment polarity),but this paper instead focuses on the origin of information diffusion,namely back to its makers and contributors.According our previous findings that have shown,at the level of Tieba's contents,the negative information or emotions spread faster than positive ones,we centre on the maker of negative message in this paper,so-called cybermobs who post aggressive,provocative or insulting remarks on social websites.We explore the different characteristics between suspected cybermobs and general netizens and then extract relative unique features of suspected cybermobs.We construct real system to identify suspected cybermob automatically using ma-chine learning method with above features,including other common features like user/content-based ones.Empirical results show that our approach can detect suspected cybermob correctly and efficiently as we evaluate it with benchmark models,and apply it to actual cases.
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