Modeling Conversation Structure and Temporal Dynamics for Jointly Predicting Rumor Stance and Veracity

Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, pages 4787–4798, Hong Kong, China, November 3–7, 2019. c⃝2019 Association for Computational Linguistics 4787 Modeling Conversation Structure and Temporal Dynamics for Jointly Predicting Rumor Stance and Veracity Penghui Wei, Nan Xu, Wenji Mao †SKL-MCCS, Institute of Automation, Chinese Academy of Sciences (CASIA) ‡University of Chinese Academy of Sciences {weipenghui2016,xunan2015,wenji.mao}@ia.ac.cn Abstract Automatically verifying rumorous informa- tion has become an important and challeng- ing task in natural language processing and so- cial media analytics. Previous studies reveal that people’s stances towards rumorous mes- sages can provide indicative clues for identi- fying the veracity of rumors, and thus deter- mining the stances of public reactions is a cru- cial preceding step for rumor veracity predic- tion. In this paper, we propose a hierarchical multi-task learning framework for jointly pre- dicting rumor stance and veracity on Twitter, which consists of two components. The bot- tom component of our framework classifies the stances of tweets in a conversation discussing a rumor via modeling the structural property based on a novel graph convolutional network. The top component predicts the rumor veracity by exploiting the temporal dynamics of stance evolution. Experimental results on two bench- mark datasets show that our method outper- forms previous methods in both rumor stance classification and veracity prediction. 1 Introduction Social media websites have become the main plat- form for users to browse information and share opinions, facilitating news dissemination greatly. However, the characteristics of social media also accelerate the rapid spread and dissemination of un- verified information, i.e., rumors (Shu et al., 2017). The definition of rumor is “items of information that are unverified at the time of posting” (Zubi- aga et al., 2018). Ubiquitous false rumors bring about harmful effects, which has seriously affected public and individual lives, and caused panic in society (Zhou and Zafarani, 2018; Sharma et al., 2019). Because online content is massive and de- bunking rumors manually is time-consuming, there is a great need for automatic methods to identify false rumors (Oshikawa et al., 2018). Timeline 1 2 3 4 5 6 Source tweet (originating a rumor): 1. Reply tweets: 2, 3, 4, 5, and 6. 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sha1_base64="DMnalIG6O30mTYHReP1CxLydbXY=">AB7nicbVDLSgMxFL1TX7W+qi7dBIvgqsyIoMtiNy4r2Ae0Q8mkmTY0jyHJCGXoR7hxoYhbv8edf2OmnYW 2HgczrmH3HuihDNjf/bK21sbm3vlHcre/sHh0fV45OUakmtE0UV7oXYUM5k7RtmeW0l2iKRcRpN5o2c7/7RLVhSj7aWUJDgceSxYxg6RuUwlBpR1Wa37dXwCtk6AgNSjQGla/BiNF0jxLODamH/iJDTOsLSOcziuD1NAEkyke076jEgtqwmyx7hxdOGWEYqXdkxYt1N+JDAtjZiJykwLbiVn1cvE/r5/a+DbMmExSyVZfhSnHFmF8tvRiGlKLJ85golmbld EJlhjYl1DFVdCsHryOulc1QO/Hjxc1xp3R1lOINzuIQAbqAB9CNhCYwjO8wpuXeC/eu/exHC15ReYU/sD7/AFd1o+T</latexit> <latexit sha1_base64="DMnalIG6O30mTYHReP1CxLydbXY=">AB7nicbVDLSgMxFL1TX7W+qi7dBIvgqsyIoMtiNy4r2Ae0Q8mkmTY0jyHJCGXoR7hxoYhbv8edf2OmnYW 2HgczrmH3HuihDNjf/bK21sbm3vlHcre/sHh0fV45OUakmtE0UV7oXYUM5k7RtmeW0l2iKRcRpN5o2c7/7RLVhSj7aWUJDgceSxYxg6RuUwlBpR1Wa37dXwCtk6AgNSjQGla/BiNF0jxLODamH/iJDTOsLSOcziuD1NAEkyke076jEgtqwmyx7hxdOGWEYqXdkxYt1N+JDAtjZiJykwLbiVn1cvE/r5/a+DbMmExSyVZfhSnHFmF8tvRiGlKLJ85golmbld EJlhjYl1DFVdCsHryOulc1QO/Hjxc1xp3R1lOINzuIQAbqAB9CNhCYwjO8wpuXeC/eu/exHC15ReYU/sD7/AFd1o+T</latexit> <latexit sha1_base64="DMnalIG6O30mTYHReP1CxLydbXY=">AB7nicbVDLSgMxFL1TX7W+qi7dBIvgqsyIoMtiNy4r2Ae0Q8mkmTY0jyHJCGXoR7hxoYhbv8edf2OmnYW 2HgczrmH3HuihDNjf/bK21sbm3vlHcre/sHh0fV45OUakmtE0UV7oXYUM5k7RtmeW0l2iKRcRpN5o2c7/7RLVhSj7aWUJDgceSxYxg6RuUwlBpR1Wa37dXwCtk6AgNSjQGla/BiNF0jxLODamH/iJDTOsLSOcziuD1NAEkyke076jEgtqwmyx7hxdOGWEYqXdkxYt1N+JDAtjZiJykwLbiVn1cvE/r5/a+DbMmExSyVZfhSnHFmF8tvRiGlKLJ85golmbld EJlhjYl1DFVdCsHryOulc1QO/Hjxc1xp3R1lOINzuIQAbqAB9CNhCYwjO8wpuXeC/eu/exHC15ReYU/sD7/AFd1o+T</latexit> <latexit sha1_base64="DMnalIG6O30mTYHReP1CxLydbXY=">AB7nicbVDLSgMxFL1TX7W+qi7dBIvgqsyIoMtiNy4r2Ae0Q8mkmTY0jyHJCGXoR7hxoYhbv8edf2OmnYW 2HgczrmH3HuihDNjf/bK21sbm3vlHcre/sHh0fV45OUakmtE0UV7oXYUM5k7RtmeW0l2iKRcRpN5o2c7/7RLVhSj7aWUJDgceSxYxg6RuUwlBpR1Wa37dXwCtk6AgNSjQGla/BiNF0jxLODamH/iJDTOsLSOcziuD1NAEkyke076jEgtqwmyx7hxdOGWEYqXdkxYt1N+JDAtjZiJykwLbiVn1cvE/r5/a+DbMmExSyVZfhSnHFmF8tvRiGlKLJ85golmbld EJlhjYl1DFVdCsHryOulc1QO/Hjxc1xp3R1lOINzuIQAbqAB9CNhCYwjO8wpuXeC/eu/exHC15ReYU/sD7/AFd1o+T</latexit> Stance 1 2 3 4 5 6 Sequential based: Temporal based: Aggregation based: 1 ! 2 ! 5, 1 ! 2 ! 4 ! 6 <latexit …

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