Research Status of Deep Learning Methods for Rumor Detection

TL;DR

This paper reviews deep learning approaches for rumor detection on social media, analyzing features, models, and methods to provide a comprehensive overview and identify future research directions.

Contribution

It systematically categorizes rumor detection methods, compares their advantages, and summarizes datasets and future challenges in the field.

Findings

Deep learning models include CNN, RNN, GNN, Transformer.

Seven main rumor detection methods identified, including adversarial and multi-task learning.

Comparison of methods' advantages and discussion of datasets and future issues.

Abstract

To manage the rumors in social media to reduce the harm of rumors in society. Many studies used methods of deep learning to detect rumors in open networks. To comprehensively sort out the research status of rumor detection from multiple perspectives, this paper analyzes the highly focused work from three perspectives: Feature Selection, Model Structure, and Research Methods. From the perspective of feature selection, we divide methods into content feature, social feature, and propagation structure feature of the rumors. Then, this work divides deep learning models of rumor detection into CNN, RNN, GNN, Transformer based on the model structure, which is convenient for comparison. Besides, this work summarizes 30 works into 7 rumor detection methods such as propagation trees, adversarial learning, cross-domain methods, multi-task learning, unsupervised and semi-supervised methods, based knowledge graph, and other methods for the first time. And compare the advantages of different methods to detect rumors. In addition, this review enumerate datasets available and discusses the potential issues and future work to help researchers advance the development of field.