In recent years, an unhealthy phenomenon characterized as the massive spread of fake news or unverified information (i.e., rumors) has become increasingly a daunting issue in human society. The rumors commonly originate from social media outlets, primarily microblogging platforms, being viral afterwards by the wild, willful propagation via a large number of participants. It is observed that rumorous posts often trigger versatile, mostly controversial stances among participating users. Thus, determining the stances on the posts in question can be pertinent to the successful detection of rumors, and vice versa. Existing studies, however, mainly regard rumor detection and stance classification as separate tasks. In this paper, we argue that they should be treated as a joint, collaborative effort, considering the strong connections between the veracity of claim and the stances expressed in responsive posts. Enlightened by the multi-task learning scheme, we propose a joint framework that unifies the two highly pertinent tasks, i.e., rumor detection and stance classification. Based on deep neural networks, we train both tasks jointly using weight sharing to extract the common and task-invariant features while each task can still learn its task-specific features. Extensive experiments on real-world datasets gathered from Twitter and news portals demonstrate that our proposed framework improves both rumor detection and stance classification tasks consistently with the help of the strong inter-task connections, achieving much better performance than state-of-the-art methods.
