MNRD: A Merged Neural Model For Rumor Detection In Social Media

Abstract

With the continuous development of social media and the diversity of its contents, rumors can quickly spread widely and cause serious damage, which makes the early detection of rumors a key task in social media analysis. Most existing methods for rumor detection exploit handcrafted features and ignore the different roles original post and retweets play in information production and spreading process. In fact, original post usually contains detailed content information about event itself, while retweets reflect the diffusion of information in online interaction. Recently, several studies manifest the effectiveness of deep neural network in rumor detection. However, these previous research has made no distinction between original post and retweets for rumor detection. In this paper, we propose a Merged Neural Rumor Detection (MNRD) model that consider three aspects of rumor data: content of original post, diffusion of retweet and user information. We propose content attention to aggregate pivotal words in original post and diffusion attention to focus on most informative retweets during the diffusion process. We also incorporate user information by identifying features to capture user’s reliability and social influence. Experimental results on real world dataset show the effectiveness of our model to detect rumors in social media.