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Modeling microscopic and macroscopic information diffusion for rumor detection
Author(s) -
Chen Xueqin,
Zhou Fan,
Zhang Fengli,
Bonsangue Marcello
Publication year - 2021
Publication title -
international journal of intelligent systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.291
H-Index - 87
eISSN - 1098-111X
pISSN - 0884-8173
DOI - 10.1002/int.22518
Subject(s) - rumor , computer science , artificial intelligence , focus (optics) , diffusion , baseline (sea) , machine learning , deep learning , data science , oceanography , physics , public relations , political science , optics , thermodynamics , geology
Researchers have exerted tremendous effort in designing ways to detect and identify rumors automatically. Traditional approaches focus on feature engineering, which requires extensive manual efforts and are difficult to generalize to different domains. Recently, deep learning solutions have emerged as the de facto methods which detect online rumors in an end‐to‐end manner. However, they still fail to fully capture the dissemination patterns of rumors. In this study, we propose a novel diffusion‐based rumor detection model, called Macroscopic and Microscopic‐aware Rumor Detection, to explore the full‐scale diffusion patterns of information. It leverages graph neural networks to learn the macroscopic diffusion of rumor propagation and capture microscopic diffusion patterns using bidirectional recurrent neural networks while taking into account the user‐time series. Moreover, it leverages knowledge distillation technique to create a more informative student model and further improve the model performance. Experiments conducted on two real‐world data sets demonstrate that our method achieves significant accuracy improvements over the state‐of‐the‐art baseline models on rumor detection.