Early detection of rumor veracity in social media

Abstract

Rumor spread has become a significant issue in online social networks (OSNs). To mitigate and limit the spread of rumors and its detrimental effects, analyzing, detecting and better understanding rumor dynamics is required. One of the critical steps of studying rumor spread is to identify the level of the rumor truthfulness in its early stage. Understanding and identifying the level of rumor truthfulness helps prevent its viral spread and minimizes the damage a rumor may cause. In this research, we aim to debunk rumors by analyzing, visualizing, and classifying the level of rumor truthfulness from a large number of users that actively engage in rumor spread. First, we create a dataset of rumors that belong to one of five categories: “False”, “Mostly False”, “True”, “Mostly True”, and “Half True”. This dataset provides intrinsic characteristics of a rumor: topics, user's sentiment, network structural and content features. Second, we analyze and visualize the characteristics of each rumor category to better understand its features. Third, using theories from social science and psychology, we build a feature set to classify those rumors and identify their truthfulness. The evaluation results on our new dataset show that the approach could effectively detect the truth of rumors as early as seven days. The proposed approach could be used as a valuable tool for existing fact-checking websites, such as Snopes.com or Politifact.com, to detect the veracity of rumors in its early stage automatically and educate OSN users to have a well-informed decision-making process.