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

arXiv: https://arxiv.org/abs/2105.04098 · HTML: https://mlsanity.com/paper/2105.04098/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 34 references that are in the map.
- First posted on arXiv 10 May 2021.

## Builds on (most cited first, 8 of 8)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Convolutional Neural Networks for Sentence Classification](https://mlsanity.com/paper/1408.5882/) (2014)
- [RumourEval 2019: Determining Rumour Veracity and Support for Rumours](https://mlsanity.com/paper/1809.06683/) (2018)
- [Jointly embedding the local and global relations of heterogeneous graph for rumor detection](https://mlsanity.com/paper/1909.04465/) (2019)
- [Weak Supervision for Fake News Detection via Reinforcement Learning](https://mlsanity.com/paper/1912.12520/) (2019)
- [Early Detection of Fake News by Utilizing the Credibility of News, Publishers, and Users Based on Weakly Supervised Learning](https://mlsanity.com/paper/2012.04233/) (2020)
- [Learning review representations from user and product level information for spam detection](https://mlsanity.com/paper/1909.04455/) (2019)

## Built on by (most cited first, 4 of 4)

- [Large Language Models Meet Stance Detection: A Survey of Tasks, Methods, Applications, Challenges and Future Directions](https://mlsanity.com/paper/2505.08464/) (2025)
- [Reinforcement Tuning for Detecting Stances and Debunking Rumors Jointly with Large Language Models](https://mlsanity.com/paper/2406.02143/) (2024)
- [Mining User-aware Multi-relations for Fake News Detection in Large Scale Online Social Networks](https://mlsanity.com/paper/2212.10778/) (2022)
- [Survival Analysis on Structured Data using Deep Reinforcement Learning](https://mlsanity.com/paper/2205.14331/) (2022)

## Abstract (arXiv)

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.