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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 35 / No. 1: AAAI-21 Technical Tracks 1

KAN: Knowledge-aware Attention Network for Fake News Detection

February 1, 2023

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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


DOI:

10.1609/aaai.v35i1.16080


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.

Topics: AAAI

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HOW TO CITE:

Yaqian Dun||Kefei Tu||Chen Chen||Chunyan Hou||Xiaojie Yuan KAN: Knowledge-aware Attention Network for Fake News Detection Proceedings of the AAAI Conference on Artificial Intelligence (2021) 81-89.

Yaqian Dun||Kefei Tu||Chen Chen||Chunyan Hou||Xiaojie Yuan KAN: Knowledge-aware Attention Network for Fake News Detection AAAI 2021, 81-89.

Yaqian Dun||Kefei Tu||Chen Chen||Chunyan Hou||Xiaojie Yuan (2021). KAN: Knowledge-aware Attention Network for Fake News Detection. Proceedings of the AAAI Conference on Artificial Intelligence, 81-89.

Yaqian Dun||Kefei Tu||Chen Chen||Chunyan Hou||Xiaojie Yuan. KAN: Knowledge-aware Attention Network for Fake News Detection. Proceedings of the AAAI Conference on Artificial Intelligence 2021 p.81-89.

Yaqian Dun||Kefei Tu||Chen Chen||Chunyan Hou||Xiaojie Yuan. 2021. KAN: Knowledge-aware Attention Network for Fake News Detection. "Proceedings of the AAAI Conference on Artificial Intelligence". 81-89.

Yaqian Dun||Kefei Tu||Chen Chen||Chunyan Hou||Xiaojie Yuan. (2021) "KAN: Knowledge-aware Attention Network for Fake News Detection", Proceedings of the AAAI Conference on Artificial Intelligence, p.81-89

Yaqian Dun||Kefei Tu||Chen Chen||Chunyan Hou||Xiaojie Yuan, "KAN: Knowledge-aware Attention Network for Fake News Detection", AAAI, p.81-89, 2021.

Yaqian Dun||Kefei Tu||Chen Chen||Chunyan Hou||Xiaojie Yuan. "KAN: Knowledge-aware Attention Network for Fake News Detection". Proceedings of the AAAI Conference on Artificial Intelligence, 2021, p.81-89.

Yaqian Dun||Kefei Tu||Chen Chen||Chunyan Hou||Xiaojie Yuan. "KAN: Knowledge-aware Attention Network for Fake News Detection". Proceedings of the AAAI Conference on Artificial Intelligence, (2021): 81-89.

Yaqian Dun||Kefei Tu||Chen Chen||Chunyan Hou||Xiaojie Yuan. KAN: Knowledge-aware Attention Network for Fake News Detection. AAAI[Internet]. 2021[cited 2023]; 81-89.


ISSN: 2374-3468


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