Mohammad Raihanul Islam
ABSTRACT
The penetration of social media has had deep and far-reaching consequences in information production and consumption. Widespread use of social media platforms has engendered malicious users and attention seekers to spread rumors and fake news. This trend is particularly evident in various microblogging platforms where news becomes viral in a matter of hours and can lead to mass panic and confusion. One intriguing fact regarding rumors and fake news is that very often rumor stories prompt users to adopt different stances about the rumor posts. Understanding user stances in rumor posts is thus very important to identify the veracity of the underlying content. While rumor veracity and stance detection have been viewed as disjoint tasks we demonstrate here how jointly learning both of them can be fruitful. In this paper, we propose RumorSleuth, a multitask deep learning model which can leverage both the textual information and user profile information to jointly identify the veracity of a rumor along with users' stances. Tests on two publicly available rumor datasets demonstrate that RumorSleuth outperforms current state-of-the-art models and achieves up to 14% performance gain in rumor veracity classification and around 6% improvement in user stance classification.
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