Dynamic Slide Window-Based Feature Scoring and Extraction for On-Line Rumor Detection with CNN

Abstract

User-generated content on social media platforms are major forces in the shaping and diffusion of popular topics. Online rumors among regular user-generated content have increased considerablely, and stimulate the diffusion of fake popular topics in the social network or even panic among people. The existing researches on rumor detection, such as the detection mechanisms based on Convolutional Neural Network (CNN) or Long Short-Term Memory (LSTM), suffer from their rough feature extraction processes, and thus need to be improved in terms of contextual feature extraction. This paper proposes a dynamic slide-window based text feature scoring and extraction mechanism, which facilitates accurate semantic structure representation. In addition, we design an effective rumor detection scheme by incorporating the proposed feature scoring and extraction with CNN. Extensive experiments on real-world datasets demonstrate that the proposed feature scoring and extraction mechanism can extract the important text features by considering their roles in on-line texts and the relations of these features, and thus the detection scheme can distinguish rumors from regular messages more accurately.