Debunking rumors on Twitter with tree transformer

• Main Authors: MA, Jing, GAO, Wei
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2020
• Subjects: Databases and Information Systems
• Online Access: https://ink.library.smu.edu.sg/sis_research/5599; https://ink.library.smu.edu.sg/context/sis_research/article/6602/viewcontent/2020.coling_main.476.pdf
• Institution: Singapore Management University

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.