Early Rumour Detection

Proceedings of NAACL-HLT 2019, pages 1614–1623 Minneapolis, Minnesota, June 2 - June 7, 2019. c⃝2019 Association for Computational Linguistics 1614 Early Rumour Detection Kaimin Zhou2,3 Chang Shu1,2 Binyang Li3⇤ Jey Han Lau2,4,5 1 School of Computer Science, University of Nottingham Ningbo China 2 DeepBrain 3 School of Information Science and Technology, University of International Relations 4 School of Computing and Information Systems, The University of Melbourne 5 IBM Research Australia will@deepbrain.ai, scxcs1@nottingham.edu.cn, byli@uir.edu.cn, jeyhan.lau@gmail.com Abstract Rumours can spread quickly through social media, and malicious ones can bring about sig- nificant economical and social impact. Moti- vated by this, our paper focuses on the task of rumour detection; particularly, we are in- terested in understanding how early we can detect them. Although there are numerous studies on rumour detection, few are con- cerned with the timing of the detection. A successfully-detected malicious rumour can still cause significant damage if it isn’t de- tected in a timely manner, and so timing is crucial. To address this, we present a novel methodology for early rumour detection. Our model treats social media posts (e.g. tweets) as a data stream and integrates reinforcement learning to learn the number minimum num- ber of posts required before we classify an event as a rumour. Experiments on Twitter and Weibo demonstrate that our model identifies rumours earlier than state-of-the-art systems while maintaining a comparable accuracy. 1 Introduction The concept of rumour has a long history, and there are various definitions from different re- search communities (Allport and Postman, 1947). In this paper, we follow a commonly accepted def- inition of rumour, that it is an unverified statement, circulating from person to person and pertaining to an object, event, or issue of public concern and it is circulating without known authority for its truth- fulness at the current time, but it may turn out to be true, or partly or entirely false; alternatively, it may also remain unresolved (Peterson and Gist, 1951; Zubiaga et al., 2018). Rumours have the potential to spread quickly through social media, and bring about significant economical and social impact. Figure 1 illustrates an example of a rumour propagating on TWIT- TER. The source message started a claim about 0 hour 4 hours 8 hours 12 hours 16 hours 20 hours 24 hours This is unbelievable, or should be. User 2 Follower Count: 3144 No excuse. User 1 Follower Count: 1222 It applies to Black people. User 3 Follower Count: 6 These days anything, especially with Stand Your Ground and even a sneeze is punishable by death. User 5 Follower Count: 6632 Anything is punishable by death if the youth is black. User 8 Follower Count: 11197 apparently it is now. User 4 Follower Count: 205 He was 18. Nothing to do with stealing candy. He was walking in the street. Horrible situation. But stop spreading false facts. User 7 Follower Count: 122 17 year old unarmed kid shot ten times by police for stealing candy. I didn't know that was punishable by death. User …

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