Dual-channel early rumor detection based on factual evidence

Abstract

Social media facilitates people's free expression and communication, but it also provides a platform for the generation and dissemination of rumors. In order to curb the spread of rumors in time and minimize their harm, early rumor detection methods were born and became a research hotspot. Early rumor detection requires a balance between accuracy and timeliness, but most existing methods only focused on accuracy and neglected efficiency. In this regard, we analyze a large number of Internet rumors and found that some rumors have been repeatedly spread after a small amount of modification. Such old rumors can be quickly and easily identified through factual evidence, while other new rumors require more clues and time to crack. Based on this insight, we propose a dual-channel early rumor detection model named ERD-DC (Early Rumor Detection-Dual-Channel), which leverages deep learning algorithms. Specifically, ERD-DC consists of two distinct channels, a fast channel referred to as channel-E (channel-Easy) and a slow channel named channel-D (channel-Difficult). In the ERD-DC, posts are divided into ‘easy-to-identify’ and ‘difficult-to-identify’ based on the availability of relevant evidence online. Subsequently, both types of posts are directed to their respective channels for detection. Channel-E focuses on identifying easy-to-identify rumors through a straightforward and swift post-evidence matching process, primarily aimed at enhancing timeliness. In parallel, channel-D is tasked with uncovering difficult-to-identify rumors, leveraging a more comprehensive analysis of user behaviors and posts transmission structures to elevate overall accuracy. Experimental results on two real datasets demonstrate that ERD-DC outperforms other state-of-the-art models significantly in early rumor detection with an accuracy rate of 84%.