TY - JOUR AU - Khoo, Ling Min Serena AU - Chieu, Hai Leong AU - Qian, Zhong AU - Jiang, Jing PY - 2020/04/03 Y2 - 2024/03/28 TI - Interpretable Rumor Detection in Microblogs by Attending to User Interactions JF - Proceedings of the AAAI Conference on Artificial Intelligence JA - AAAI VL - 34 IS - 05 SE - AAAI Technical Track: Natural Language Processing DO - 10.1609/aaai.v34i05.6405 UR - https://ojs.aaai.org/index.php/AAAI/article/view/6405 SP - 8783-8790 AB - <p>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.</p> ER -