Mining Semantic Information in Rumor Detection via a Deep Visual Perception Based Recurrent Neural Networks

Abstract

Rumor detection becomes a major issue concerning the public and government as the proliferation of social media in information dissemination. However, most existing methods only extract hand-crafted features, far from adequate in interpreting semantics latent in texts. For social events, there also exists rich social contextual information and highlevel interactions among significant features, which provides cues for interpreting semantics. In this paper, we propose a novel attention learning framework via deep visual perception based recurrent neural network (ViP-RNN), considering both high-level feature interactions and contextual information. In particular, the proposed model is based on RNN for capturing the long-distance temporal dependencies of contextual information of relevant posts and composing low-level lexical features into high-level semantic interactions hierarchically by visual perception of convolutional neural network (CNN). To incorporate information learned by RNN and CNN, we combine convolutional and recurrent layers into one model so that the model can capture a discriminative semantic representation of social events more efficiently by utilizing visual perception attention vector i.e. outputs of CNN to align long-distance temporal dependencies. We conduct experiments on real datasets collected from social media websites, which demonstrates the effectiveness of our approach and the merits of model integration.