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首页> 外文会议>Asia-Pacific Signal and Information Processing Association Annual Summit and Conference >WeChat Toxic Article Detection: A Data-Driven Machine Learning Approach
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WeChat Toxic Article Detection: A Data-Driven Machine Learning Approach

机译：微信有毒物品检测：一种数据驱动的机器学习方法

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摘要

Recently, toxic information detection has attracted tremendous amounts of research interest because of the popularity of social networks and the widespread of toxic information which may have dire consequences to the public. Existing work extensively studies toxic article detection in open social networks from information diffusion perspective. However, in closed social networks as exemplified by WeChat Moments (WM), the diffusion process is uneasily visible. To tackle the toxic article detection problem in closed social networks, in this paper we empirically study the articles spread in WM which is based on the largest Chinese social platform WeChat. In particular, we systematically analyze users' behavior and text information of normal and toxic articles and identify a striking difference between them. Furthermore, we design a new model named MAT-LSTM which can well capture the impact of different kinds of text information. To improve the performance of automatic toxic article detection, we propose XMATL framework which is enhanced from MAT-LSTM and can utilize text information and users' behavior characteristics in a holistic manner. We conduct extensive experiments using two real-world datasets and demonstrate that our proposed model can effectively detect toxic articles in WM and achieve outstanding performance gain over the classic methods.
机译：近来，由于社交网络的普及以及有毒信息的广泛传播，有毒信息的检测已经引起了巨大的研究兴趣，这可能对公众造成可怕的后果。现有工作从信息传播的角度广泛研究了开放式社交网络中有毒物品的检测。但是，在以微信矩（WM）为代表的封闭式社交网络中，扩散过程不可见。为了解决封闭式社交网络中的有毒物品检测问题，本文以中国最大的社交平台微信为基础，对WM中传播的文章进行了实证研究。特别是，我们系统地分析用户的正常行为和有毒物品的行为和文字信息，并找出它们之间的显着差异。此外，我们设计了一个名为MAT-LSTM的新模型，该模型可以很好地捕获各种文本信息的影响。为了提高有毒物品自动检测的性能，我们提出了XMATL框架，该框架是从MAT-LSTM增强而来的，可以全面利用文本信息和用户的行为特征。我们使用两个现实世界的数据集进行了广泛的实验，并证明了我们提出的模型可以有效地检测WM中的有毒物品，并且比经典方法具有出色的性能提升。

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