Multi-task Learning for Stance and Early Rumor Detection

Rumor detection and stance classification are specialized areas in the field of Information Retrieval and Natural Language Processing. Judging the stances of public response is viewed as an important preceding step of rumor veracity prediction. In this paper we develop a reinforcement learning-based multi-task learning for rumor early detection (RL_MT_RED), which formulates the closely correlated rumor detection and stance classification problems as a multi-task learning and jointly learn them. Otherwise, in order to realize the early rumour detection, RL_MT_RED integrates reinforcement learning to control multi-task learning, and realizes to dynamically set credible checkpoint. The experimental results indicate that our proposed method is superior to the traditional methods.