Interpretable Rumor Detection in Microblogs by Attending to User Interactions

• URL: http://arxiv.org/abs/2001.10667v1
• Date: Wed, 29 Jan 2020 02:37:11 GMT
• Title: Interpretable Rumor Detection in Microblogs by Attending to User Interactions
• Authors: Ling Min Serena Khoo, Hai Leong Chieu, Zhong Qian and Jing Jiang
• Abstract summary: We address rumor detection by learning to differentiate between the community's response to real and fake claims in microblogs. We propose a post-level attention model (PLAN) to model long distance interactions between tweets. We show that our best models outperform current state-of-the-art models for both data sets.
• Score: 11.881745037884953
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We address rumor detection by learning to differentiate between the community's response to real and fake claims in microblogs. Existing state-of-the-art models are based on tree models that model conversational trees. However, in social media, a user posting a reply might be replying to the entire thread rather than to a specific user. We propose a post-level attention model (PLAN) to model long distance interactions between tweets with the multi-head attention mechanism in a transformer network. We investigated variants of this model: (1) a structure aware self-attention model (StA-PLAN) that incorporates tree structure information in the transformer network, and (2) a hierarchical token and post-level attention model (StA-HiTPLAN) that learns a sentence representation with token-level self-attention. To the best of our knowledge, we are the first to evaluate our models on two rumor detection data sets: the PHEME data set as well as the Twitter15 and Twitter16 data sets. We show that our best models outperform current state-of-the-art models for both data sets. Moreover, the attention mechanism allows us to explain rumor detection predictions at both token-level and post-level.

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