论文标题
深度学习方法的研究状态
Research Status of Deep Learning Methods for Rumor Detection
论文作者
论文摘要
管理社交媒体中的谣言，以减少谣言在社会上的危害。许多研究使用深度学习的方法来检测开放网络中的谣言。为了全面地从多个角度列出谣言检测的研究状态，本文从三个角度分析了高度集中的工作：特征选择，模型结构和研究方法。从特征选择的角度来看，我们将方法分为谣言的内容功能，社交特征和传播结构。然后，这项工作将基于模型结构的谣言检测的深度学习模型分为CNN，RNN，GNN，变压器，这很方便地进行比较。此外，这项工作总结了7种谣言检测方法，例如传播树，对抗性学习，跨域方法，多任务学习，无监督和半监督的方法，基于知识图和其他第一次方法。并比较不同方法检测谣言的优势。此外，这篇综述列举了可用的数据集，并讨论了潜在的问题和未来工作，以帮助研究人员推进领域的发展。
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.
