Wu Zhiyuan, Pi Dechang, Chen Junfu, Xie Meng, Cao Jianjun
College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu, China.
The Sixty-third Research Institute, National University of Defence Technology, Nanjing, Jiangsu, China.
Expert Syst Appl. 2020 Nov 15;158:113595. doi: 10.1016/j.eswa.2020.113595. Epub 2020 Jun 5.
Rumors on social media have always been an important issue that seriously endangers social security. Researches on timely and effective detection of rumors have aroused lots of interest in both academia and industry. At present, most existing methods identify rumors based solely on the linguistic information without considering the temporal dynamics and propagation patterns. In this work, we aim to solve rumor detection task under the framework of representation learning. We first propose a novel way to construct the propagation graph by following the propagation structure (who replies to whom) of posts on Twitter. Then we propose a gated graph neural network based algorithm called PGNN, which can generate powerful representations for each node in the propagation graph. The proposed PGNN algorithm repeatedly updates node representations by exchanging information between the neighbor nodes via relation paths within a limited time steps. On this basis, we propose two models, namely GLO-PGNN (rumor detection model based on the embedding with ) and ENS-PGNN (rumor detection model based on the with ). They respectively adopt different classification strategies for rumor detection task, and further improve the performance by including attention mechanism to dynamically adjust the weight of each node in the propagation graph. Experiments on a real-world Twitter dataset demonstrate that our proposed models achieve much better performance than state-of-the-art methods both on the rumor detection task and early detection task.
社交媒体上的谣言一直是严重危害社会安全的重要问题。对谣言进行及时有效检测的研究在学术界和工业界都引起了广泛关注。目前，大多数现有方法仅基于语言信息来识别谣言，而没有考虑时间动态和传播模式。在这项工作中，我们旨在解决表示学习框架下的谣言检测任务。我们首先提出一种新颖的方法，通过遵循Twitter上帖子的传播结构（谁回复谁）来构建传播图。然后我们提出一种基于门控图神经网络的算法PGNN，它可以为传播图中的每个节点生成强大的表示。所提出的PGNN算法通过在有限的时间步长内通过关系路径在邻居节点之间交换信息来反复更新节点表示。在此基础上，我们提出了两个模型，即GLO-PGNN（基于 嵌入的谣言检测模型）和ENS-PGNN（基于 与 的谣言检测模型）。它们分别对谣言检测任务采用不同的分类策略，并通过引入注意力机制来动态调整传播图中每个节点的权重进一步提高性能。在一个真实的Twitter数据集上进行的实验表明，我们提出的模型在谣言检测任务和早期检测任务上都比现有方法取得了更好的性能。
