Debunking Rumors on Twitter with Tree Transformer

Jing Ma, Wei Gao

Research output: Chapter in book/report/conference proceedingConference contribution


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.
Original languageEnglish
Title of host publicationProceedings of the 28th International Conference on Computational Linguistics, COLING 2020
EditorsDonia Scott, Nuria Bel, Chengqing Zong
PublisherInternational Committee on Computational Linguistics
Number of pages12
ISBN (Print)9781952148279
Publication statusPublished - Dec 2020
EventThe 28th International Conference on Computational Linguistics - Online, Barcelona, Spain
Duration: 8 Dec 202013 Dec 2020


ConferenceThe 28th International Conference on Computational Linguistics
Abbreviated titleCOLING 2020
Internet address

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