Research Status of Deep Learning Methods for Rumor Detection
arXiv:2204.11540 · doi:10.1007/s11042-022-12800-8
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.
Accepted by MTAP
References in corpus (10)
- Very Deep Convolutional Networks for Large-Scale Image Recognition
- Distributed Representations of Sentences and Documents
- Deep Fragment Embeddings for Bidirectional Image Sentence Mapping
- Fake News Detection on Social Media: A Data Mining Perspective
- Learning Reporting Dynamics during Breaking News for Rumour Detection in Social Media
- Graph Neural Networks with Continual Learning for Fake News Detection from Social Media
- A Kernel of Truth: Determining Rumor Veracity on Twitter by Diffusion Pattern Alone
- RP-DNN: A Tweet level propagation context based deep neural networks for early rumor detection in Social Media
- Structured Model Pruning of Convolutional Networks on Tensor Processing Units
- On the Role of Images for Analyzing Claims in Social Media