Rumor Detection Via Assessing the Spreading Propensity of Users

Abstract

The explosion of rumors on social media has adversely affected cyber security and our lives, increasing the urgent demand for rumor detection. Existing detection methods focus on exploring signals of deception from textual contents and rumor propagation structures, without fully considering user’s long-term spreading propensity. Sociology and psychology have demonstrated that when a rumor satisfies a user’s inner requirements, he/she has more propensity to spread it. User context, such as historical posts, offers extensive details about propensities for spreading rumors, which has great potential to promote rumor detection. Therefore, we explore a new feature space by extracting the spreading propensity from user context, and combine it with social interaction information to construct a creative detection algorithm. Experiments on three Twitter datasets show that our approach achieves significant improvements compared to strong baselines and displays a superior capacity for detecting rumors at early stages. Our code is publicly released at https://github.com/Coder-HenryZa/RDSPU.