Debunking Rumors on Twitter with Tree Transformer

Proceedings of the 28th International Conference on Computational Linguistics, pages 5455–5466 Barcelona, Spain (Online), December 8-13, 2020 5455 Debunking Rumors on Twitter with Tree Transformer Jing Ma Dept. of Computer Science Hong Kong Baptist University Hong Kong majing@comp.hkbu.edu.hk Wei Gao School of Information Systems Singapore Management University Singapore weigao@smu.edu.sg Abstract Rumors are manufactured with no respect for accuracy, but can circulate quickly and widely by “word-of-post” through social media conversations. Conversation tree encodes important information indicative of the credibility of rumor. Existing conversation-based techniques for rumor detection either just strictly follow tree edges or treat all the posts fully-connected during feature learning. In this paper, we propose a novel detection model based on tree transformer to better utilize user interactions in the dialogue where post-level self-attention plays the key role for aggregating the intra-/inter-subtree stances. Experimental results on the TWITTER and PHEME datasets show that the proposed approach consistently improves rumor detection performance. 1 Introduction Online rumor perhaps is one of the most prevalent social diseases in the era of social media. An immediate example we are witnessing is the unprecedented information disorder represented by various rumors, conspiracy theories, hoaxes, fake news, etc. in parallel with the worldwide pandemic of COVID19. In different places, a number of people were hospitalized or even died for drinking bootleg alcohol to prevent coronavirous infection, resulting from a false rumor attack on gullible public claiming that “smoking, methanol or cocaine can cure for the virus”1. Automatic rumor debunking is at the core of battle against such massive disorder of information especially in the midst of crisis. Rumor debunking aims to determine the veracity of a given topic or a claim. Fact-checking websites, such as snopes.com and politifact.com, employ manual verification and investigative journalism, which is prone to low efficiency and poor coverage. For automated approaches, prior studies focus on engineering or learning features from sequential microblog streams (Castillo et al., 2011; Yang et al., 2012; Kwon et al., 2013; Liu et al., 2015; Ma et al., 2015; Ma et al., 2016; Yu et al., 2017). More recently, structure-based learning based on structured neural networks are proposed to capture the interactive characteristics of rumor diffusion, such as tree kernel (Ma et al., 2017), recursive neural network (Ma et al., 2018) and tree LSTM model (Kumar and Carley, 2019). Khoo et al. (2020) proposed to model potential dependencies between any two microblog posts with the post-level self-attention networks (PLAN), which has achieved the state-of-the-art detection performance. The PLAN model essentially treats the input tweets as a fully connected graph, by assuming that a user may not be directed solely at the tweet being replied considering the content created could also be applicable to other tweets in the thread (Khoo et al., 2020). Also, the representation of posts is enhanced by leveraging the strength of transformer’s encoding architecture. Nevertheless, we argue that such full connection which ignores the specific targets of replies in the hierarchy could create salient issues on post representation learning, especially in the vein …

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