Adaptive Slide Window-Based Feature Cognition for Deceptive Information Identification

Numerous false information come up to be the primary threat for regular communication and cooperation, which always have similar expressions and diffusion patterns with regular information. To recognize deceptive information and alleviate their threat against business and economy, a number of research approaches for deceptive information identification have been proposed. However, these approaches suffer from their rough feature extraction processes, and thus cannot distinguish deceptive information from others. We introduce an adaptive slide-window based feature extraction, which captures semantic features and eliminates the trivial parts from them with adaptive slide windows on sentence elements, in order to facilitate accurate semantic structure representation of various texts. In addition, we propose a deep deceptive information identification model based on the proposed feature extraction scheme. Experiments on three real-world datasets demonstrate that the proposed deep deceptive information identification model can distinguish deceptive information from regular information accurately by extracting the significant features instead of trivial common expressions.