Fully automated rumor defeating is meaningful for reducing hazards of misinformation in social networks. As one of the automated approaches, content-based rumor defeating is a pipeline that could be divided into four sequential sub-tasks: detection, tracking, sentence classification, and veracity. Specifically, rumor tracking gathers relevant posts and filters unrelated posts for a potential rumor news, which is significant for rumor defeating and has not been studied extensively. However, the existing proposals only consider rumor tracking as an auxiliary task in multi-task learning without special optimization, therefore restraining the accuracy of tracking performance. To this end, we propose a deep reinforcement learning based ensemble model for rumor tracking (RL-ERT), which aggregates multiple components by a weight-tuning policy network, and utilizes specific social features to improve the performance. Finally, we conduct experiments on public datasets and the experimental results show the superiority of RL-ERT on efficiency and effectiveness.

全自动的谣言打败对于减少社交网络中错误信息的危害是有意义的。作为一种自动方法，基于内容的谣言打败是一种管道，可以分为四个顺序的子任务：检测，跟踪，句子分类和准确性。具体而言，谣言跟踪会收集相关帖子，并过滤不相关的帖子以查找潜在的谣言新闻，这对于消除谣言很重要，并且尚未得到广泛研究。然而，现有的建议仅将谣言跟踪作为多任务学习中的辅助任务，而没有进行特殊的优化，从而限制了跟踪性能的准确性。为此，我们提出了一种基于深度强化学习的谣言跟踪集成模型（RL-ERT），该模型通过权重调整策略网络汇总多个组件，并利用特定的社交功能来提高效果。最后，我们在公共数据集上进行了实验，实验结果证明了RL-ERT在效率和有效性方面的优越性。