UTCNN: a Deep Learning Model of Stance Classification on Social Media Text

Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 1635–1645, Osaka, Japan, December 11-17 2016. UTCNN: a Deep Learning Model of Stance Classification on Social Media Text Wei-Fan Chen Institute of Information Science, Academia Sinica, Taipei, Taiwan. viericwf@iis.sinica.edu.tw Lun-Wei Ku Institute of Information Science, Academia Sinica, Taipei, Taiwan. lwku@iis.sinica.edu.tw Abstract Most neural network models for document classification on social media focus on text infor- mation to the neglect of other information on these platforms. In this paper, we classify post stance on social media channels and develop UTCNN, a neural network model that incorporates user tastes, topic tastes, and user comments on posts. UTCNN not only works on social media texts, but also analyzes texts in forums and message boards. Experiments performed on Chinese Facebook data and English online debate forum data show that UTCNN achieves a 0.755 macro- average f-score for supportive, neutral, and unsupportive stance classes on Facebook data, which is significantly better than models in which either user, topic, or comment information is with- held. This model design greatly mitigates the lack of data for the minor class without the use of oversampling. In addition, UTCNN yields a 0.842 accuracy on English online debate forum data, which also significantly outperforms results from previous work as well as other deep learning models, showing that UTCNN performs well regardless of language or platform. 1 Introduction Deep neural networks have been widely used in text classification and have achieved promising results (Lai et al., 2015; Ren et al., 2016; Huang et al., 2016). Most focus on content information and use models such as convolutional neural networks (CNN) (Kim, 2014) or recursive neural networks (Socher et al., 2013). However, for user-generated posts on social media like Facebook or Twitter, there is more information that should not be ignored. On social media platforms, a user can act either as the author of a post or as a reader who expresses his or her comments about the post. In this paper, we classify posts taking into account post authorship, likes, topics, and comments. In particular, users and their “likes” hold strong potential for text mining. For example, given a set of posts that are related to a specific topic, a user’s likes and dislikes provide clues for stance labeling. From a user point of view, users with positive attitudes toward the issue leave positive comments on the posts with praise or even just the post’s content; from a post point of view, positive posts attract users who hold positive stances. We also investigate the influence of topics: different topics are associated with different stance labeling tendencies and word usage. For example we discuss women’s rights and unwanted babies on the topic of abortion, but we criticize medicine usage or crime when on the topic of marijuana (Hasan and Ng, 2014). Even for posts on a specific topic like nuclear power, a variety of arguments are raised: green energy, radiation, air pollution, and so on. As for comments, we …

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