Rumor Detection by Exploiting User Credibility Information, Attention and Multi-task Learning

Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 1173–1179 Florence, Italy, July 28 - August 2, 2019. c⃝2019 Association for Computational Linguistics 1173 Abstract In this study, we propose a new multi-task learning approach for rumor detection and stance classification tasks. This neural network model has a shared layer and two task specific layers. We incorporate the user credibility information into the rumor detection layer, and we also apply attention mechanism in the rumor detection process. The attended information include not only the hidden states in the rumor detection layer, but also the hidden states from the stance detection layer. The experiments on two datasets show that our proposed model outperforms the state-of-the-art rumor detection approaches. 1 Introduction Social media platforms, such as Twitter, Reddit and Facebook, do not always pose authentic information. Rumors sometimes may spread quickly over these platforms, and they usually spread fear or hate. Therefore, rumor detection and verification has gained great interest recently. Social media platforms and government authorities are also taking great efforts to defeat the negative impacts of rumors. Rumor Detection: Rumor definition varies over different publications. The lack of consistency makes it difficult to do a head-to- head comparison between existing methods. In this paper, a rumor is defined as a statement whose truth value is true, unverified or false (Qazvinian et al., 2011). When a rumor’s veracity value is false, some studies call it “false rumor” or “fake news”. However, many previous studies give “fake news” a stricter definition: fake news is a news article published by a news outlet that is intentionally and verifiably false (Shu et al., 2017; Zubiaga et al., 2018). The focus of this study is rumor on social media, not fake news. There are also different definitions for rumor detection. In some studies, rumor detection is defined as determining if a story or online post is a rumor or non-rumor (i.e. a real story, a news article), and the task of determining the veracity of a rumor (true, false or unverified) is defined as rumor verification (Zubiaga et al., 2016; Kochkina et al., 2018). But in this paper, as well as in many previous studies (Ma et al., 2016; Shu et al, 2017), rumor detection is defined as determining the veracity value of a rumor. This means it is the same as rumor verification defined in some other studies. Rumor detection and rumor verification will be used interchangeably in this paper. Zubiaga et al. (2018a) consider the rumor resolution process as a pipeline involving four sub-tasks: (1) rumor identification, determining whether a claim is worth verifying rather than the expression of an opinion, i.e. checking a claim is rumor or non-rumor; (2) rumor tracking, collecting opinions on a rumor as it unfolds; (3) stance classification, determining the attitude of users towards the truthfulness of the rumor, and (4) rumor verification, the ultimate step where the veracity value of the rumor is predicted. This study involves the last two tasks: stance classification …

Content truncated to 500 words