Exploiting Microblog Conversation Structures to Detect Rumors

Proceedings of the 28th International Conference on Computational Linguistics, pages 5420–5429 Barcelona, Spain (Online), December 8-13, 2020 5420 Exploiting Microblog Conversation Structures to Detect Rumors Jiawen Li*, Yudianto Sujana*, Hung-Yu Kao Intelligent Knowledge Management Lab Department of Computer Science and Information Engineering National Cheng Kung University Tainan, Taiwan p78073012@gs.ncku.edu.tw,yudianto.sujana@staff.uns.ac.id, hykao@mail.ncku.edu.tw Abstract As one of the most popular social media platforms, Twitter has become a primary source of information for many people. Unfortunately, both valid information and rumors are propagated on Twitter due to the lack of an automatic information verification system. Twitter users com- municate by replying to other users' messages, forming a conversation structure. Using this structure, users can decide whether the information in the source tweet is a rumor by reading the tweet’s replies, which voice other users’ stances on the tweet. The majority of rumor detection researchers process such tweets based on time, ignoring the conversation structure. To reap the benefits of the Twitter conversation structure, we developed a model to detect rumors by mod- eling conversation structure as a graph. Thus, our model’s improved representation of the con- versation structure enhances its rumor detection accuracy. The experimental results on two ru- mor datasets show that our model outperforms several baseline models, including a state-of-the- art model. 1 Introduction Social media platforms have become a primary source of information due to the ease of sharing infor- mation they provide. The latest survey from the Pew Research Center states that 68% of American adults occasionally read news on social media platforms (Holcomb et al., 2013). However, the credibility of the massive amount of news propagated through social media is questionable due to the absence of editors who can validate it (Zubiaga et al., 2018). As a result, social media platforms have become per- fect avenues for spreading unverified information and rumors. Users on social media platforms communicate by replying to other users' posts, repeatedly respond- ing to one another, forming a conversation structure. The conversation structure established by social media consists of a tree representation of the information distributed by users posting at a specific time in response to a source post. (Belkaroui et al., 2014; Cogan et al., 2012; Magnani et al., 2011). Further- more, Pace et al. (2016) distinguish two types of conversation: dialogic (horizontal conversation among users) and dialectic (vertical conversation with a source post) conversations. Conversations on social media can influence users' perception of the information within them (Pace et al., 2016). For example, Figure 1 shows a rumor conversation on Twitter in which users give opinions, make conjectures, or supply evidence in reply to the source post or other users’ replies. Users can obtain clues about the truth of the source post by reading the replies in the conversation structure. Ma et al. (2018) refer to this self-correcting mechanism in their research. However, the majority of users disregard such comments, preferring to immediately share tweets; thus, this self-correcting mechanism cannot * Equal contributions. This work is licensed under a Creative Commons Attribution 4.0 …

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