Camouflaged Chinese Spam Content Detection with Semi-supervised Generative Active Learning

Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 3080–3085 July 5 - 10, 2020. c⃝2020 Association for Computational Linguistics 3080 Camouflaged Chinese Spam Content Detection with Semi-supervised Generative Active Learning Zhuoren Jiang1∗, Zhe Gao2∗, Yu Duan2, Yangyang Kang2, Changlong Sun2, Qiong Zhang2, Xiaozhong Liu3† 1School of Public Affairs, Zhejiang University, Hangzhou, China 2Alibaba Group, Hangzhou & Sunnyvale, China & USA 3School of Informatics, Computing and Engineering, IUB, Bloomington, USA jiangzhuoren@zju.edu.cn,gaozhe.gz@alibaba-inc.com {derrick.dy,yangyang.kangyy,qz.zhang}@alibaba-inc.com, changlong.scl@taobao.com,liu237@indiana.edu Abstract We propose a Semi-supervIsed GeNerative Active Learning (SIGNAL) model to address the imbalance, efficiency, and text camou- flage problems of Chinese text spam detec- tion task. A “self-diversity” criterion is pro- posed for measuring the “worthiness” of a candidate for annotation. A semi-supervised variational autoencoder with masked attention learning approach and a character variation graph-enhanced augmentation procedure are proposed for data augmentation. The prelim- inary experiment demonstrates the proposed SIGNAL model is not only sensitive to spam sample selection, but also can improve the per- formance of a series of conventional active learning models for Chinese spam detection task. To the best of our knowledge, this is the first work to integrate active learning and semi- supervised generative learning for text spam detection. 1 Introduction The recent successes of learning-based models all share the same prerequisite: a decent labeled train- ing dataset is available for a given task (Jiang et al., 2019b; Arora and Agarwal, 2007). However, the annotating process can be “a tedious, laborious, and time consuming task for humans” (Sharma et al., 2015). To achieve high task performance with low labeling cost, (pool-based) active learning (Cohn et al., 1996) algorithms are proposed to se- lect the most representative and informative sample to be labeled by human oracles (Druck et al., 2009). Although effective in general, in Chinese text spam detection context, the following reasons make the active learning a challenging task: ∗These two authors contributed equally to this research. †Corresponding author Imbalance: in reality, the ratio of spam sam- ples to normal ones is very imbalanced. For in- stance, in North America, “much less than 1% of SMS messages were spam” (Almeida et al., 2013). As a result, the active learning model should be more sensitive to spam samples. The general ac- tive learning methods, e.g., (Lewis and Gale, 1994; Li and Guo, 2013; Roth and Small, 2006), can hardly address this problem. Efficiency: when competing with anti-spam models, spammers are constantly creating new forms for spam texts (Xie et al., 2012; Jiang et al., 2019a). The amount of unlabeled samples is huge and keeps increasing. Classical diversity-based approach (Brinker, 2003; Xu et al., 2003), which iteratively compares each unlabeled sample with each labeled sample to se- lect the most “diverse” ones for annotating, will perform poorly as its computational complexity is O(n2). An efficient-oriented active learning algo- rithm is needed. Camouflage1: Chinese character has glyph and phonetic variations (Norman, 1988), e.g., “账 (account)” and “帐(curtain)” have the sim- ilar structure and pronunciation. Spammers can take advantage of this characteristic …

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