  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.

Computation and Language;Artificial Intelligence