KAN: Knowledge-aware Attention Network for Fake News Detection

Authors

  • Yaqian Dun College of Computer Science, Nankai University, Tianjin, China Tianjin Key Laboratory of Network and Data Security Technology, Tianjin, China
  • Kefei Tu College of Computer Science, Nankai University, Tianjin, China Tianjin Key Laboratory of Network and Data Security Technology, Tianjin, China
  • Chen Chen College of Computer Science, Nankai University, Tianjin, China Tianjin Key Laboratory of Network and Data Security Technology, Tianjin, China
  • Chunyan Hou School of Computer Science and Engineering, Tianjin University of Technology, Tianjin, China
  • Xiaojie Yuan College of Computer Science, Nankai University, Tianjin, China Tianjin Key Laboratory of Network and Data Security Technology, Tianjin, China

Keywords:

Misinformation & Fake News

Abstract

The explosive growth of fake news on social media has drawn great concern both from industrial and academic communities. There has been an increasing demand for fake news detection due to its detrimental effects. Generally, news content is condensed and full of knowledge entities. However, existing methods usually focus on the textual contents and social context, and ignore the knowledge-level relationships among news entities. To address this limitation, in this paper, we propose a novel Knowledge-aware Attention Network (KAN) that incorporates external knowledge from knowledge graph for fake news detection. Firstly, we identify entity mentions in news contents and align them with the entities in knowledge graph. Then, the entities and their contexts are used as external knowledge to provide complementary information. Finally, we design News towards Entities (N-E) attention and News towards Entities and Entity Contexts (N-E^2C) attention to measure the importances of knowledge. Thus, our proposed model can incorporate both semantic-level and knowledge-level representations of news to detect fake news. Experimental results on three public datasets show that our model outperforms the state-of-the-art methods, and also validate the effectiveness of knowledge attention.

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Published

2021-05-18

How to Cite

Dun, Y., Tu, K., Chen, C., Hou, C., & Yuan, X. (2021). KAN: Knowledge-aware Attention Network for Fake News Detection. Proceedings of the AAAI Conference on Artificial Intelligence, 35(1), 81-89. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/16080

Issue

Section

AAAI Technical Track on Application Domains