Cui Wei, Shang Mingsheng
College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, China.
School of Electronic Information and Communication Engineering, Chongqing Aerospace Polytechnic, Chongqing, China.
J Big Data. 2023;10(1):45. doi: 10.1186/s40537-023-00725-4. Epub 2023 Apr 14.
Rumor posts have received substantial attention with the rapid development of online and social media platforms. The automatic detection of rumor from posts has emerged as a major concern for the general public, the government, and social media platforms. Most existing methods focus on the linguistic and semantic aspects of posts content, while ignoring knowledge entities and concepts hidden within the article which facilitate rumor detection. To address these limitations, in this paper, we propose a novel end-to-end attention and graph-based neural network model (KAGN), which incorporates external knowledge from the knowledge graphs to detect rumor. Specifically, given the post's sparse and ambiguous semantics, we identify entity mentions in the post's content and link them to entities and concepts in the knowledge graphs, which serve as complementary semantic information for the post text. To effectively inject external knowledge into textual representations, we develop a knowledge-aware attention mechanism to fuse local knowledge. Additionally, we construct a graph consisting of posts texts, entities, and concepts, which is fed to graph convolutional networks to explore long-range knowledge through graph structure. Our proposed model can therefore detect rumor by combining semantic-level and knowledge-level representations of posts. Extensive experiments on four publicly available real-world datasets show that KAGN outperforms or is comparable to other state-of-the-art methods, and also validate the effectiveness of knowledge.
随着网络和社交媒体平台的快速发展，谣言帖子受到了广泛关注。从帖子中自动检测谣言已成为公众、政府和社交媒体平台主要关注的问题。大多数现有方法侧重于帖子内容的语言和语义方面，而忽略了文章中隐藏的有助于谣言检测的知识实体和概念。为了解决这些局限性，在本文中，我们提出了一种新颖的基于注意力和图的端到端神经网络模型（KAGN），该模型结合知识图谱中的外部知识来检测谣言。具体而言，鉴于帖子语义的稀疏性和模糊性，我们识别帖子内容中的实体提及，并将它们与知识图谱中的实体和概念链接起来，这些实体和概念作为帖子文本的补充语义信息。为了有效地将外部知识注入文本表示中，我们开发了一种知识感知注意力机制来融合局部知识。此外，我们构建了一个由帖子文本、实体和概念组成的图，将其输入到图卷积网络中，通过图结构探索长距离知识。因此，我们提出的模型可以通过结合帖子的语义级和知识级表示来检测谣言。在四个公开可用的真实世界数据集上进行的大量实验表明，KAGN优于或可与其他现有最先进方法相媲美，同时也验证了知识的有效性。
