SRLF: A Stance-aware Reinforcement Learning Framework for Content-based Rumor Detection on Social Media

• URL: http://arxiv.org/abs/2105.04098v1
• Date: Mon, 10 May 2021 03:58:34 GMT
• Title: SRLF: A Stance-aware Reinforcement Learning Framework for Content-based Rumor Detection on Social Media
• Authors: Chunyuan Yuan, Wanhui Qian, Qianwen Ma, Wei Zhou, Songlin Hu
• Abstract summary: Early content-based methods focused on finding clues from text and user profiles for rumor detection. Recent studies combine the stances of users' comments with news content to capture the difference between true and false rumors. We propose a novel Stance-aware Reinforcement Learning Framework (SRLF) to select high-quality labeled stance data for model training and rumor detection.
• Score: 15.985224010346593
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The rapid development of social media changes the lifestyle of people and simultaneously provides an ideal place for publishing and disseminating rumors, which severely exacerbates social panic and triggers a crisis of social trust. Early content-based methods focused on finding clues from the text and user profiles for rumor detection. Recent studies combine the stances of users' comments with news content to capture the difference between true and false rumors. Although the user's stance is effective for rumor detection, the manual labeling process is time-consuming and labor-intensive, which limits the application of utilizing it to facilitate rumor detection. In this paper, we first finetune a pre-trained BERT model on a small labeled dataset and leverage this model to annotate weak stance labels for users' comment data to overcome the problem mentioned above. Then, we propose a novel Stance-aware Reinforcement Learning Framework (SRLF) to select high-quality labeled stance data for model training and rumor detection. Both the stance selection and rumor detection tasks are optimized simultaneously to promote both tasks mutually. We conduct experiments on two commonly used real-world datasets. The experimental results demonstrate that our framework outperforms the state-of-the-art models significantly, which confirms the effectiveness of the proposed framework.

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