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The rapid development of social media changes the lifestyle of people andsimultaneously 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 userprofiles for rumor detection. Recent studies combine the stances of users'comments with news content to capture the difference between true and falserumors. Although the user's stance is effective for rumor detection, the manuallabeling process is time-consuming and labor-intensive, which limits theapplication of utilizing it to facilitate rumor detection.  In this paper, we first finetune a pre-trained BERT model on a small labeleddataset and leverage this model to annotate weak stance labels for users'comment data to overcome the problem mentioned above. Then, we propose a novelStance-aware Reinforcement Learning Framework (SRLF) to select high-qualitylabeled stance data for model training and rumor detection. Both the stanceselection and rumor detection tasks are optimized simultaneously to promoteboth tasks mutually. We conduct experiments on two commonly used real-worlddatasets. The experimental results demonstrate that our framework outperformsthe state-of-the-art models significantly, which confirms the effectiveness ofthe proposed framework.

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