Research on fake news detection method based on multi-level semantic enhancement

Most of the existing fake news detection methods focus on the feature information at the news performance level, and pay insufficient attention to the semantic information of the news content, which cannot fully obtain the semantic information in the news. Therefore, this paper proposes a multi-leve...

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Bibliographic Details
Main Authors: Yin, Xinyan, Sun, Tao, Yang, Chunyan, Zhang, Zihao, Zhang, Xiang, Su, Mengli
Format: Conference Proceeding
Language:English
Subjects:
Benchmark testing
Data mining
Deep learning
Fake news
Fake news detection
Feature extraction
Multimodal
Neural networks
Semantic enhancement
Semantics
Visualization
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Summary:Most of the existing fake news detection methods focus on the feature information at the news performance level, and pay insufficient attention to the semantic information of the news content, which cannot fully obtain the semantic information in the news. Therefore, this paper proposes a multi-level semantic enhanced fake news detection framework (MLSED), which imitates the way modern people read news. Aiming at the two main aspects of semantic information including entity objects and event topics, the core semantic information in news content is gradually obtained by mutual enhancement to detect fake news. Extensive experiments on two real datasets show that MLSED can fully capture the semantic information of news for fake news detection, and outperforms state-of-the-art methods.
ISSN:2161-4407
DOI:10.1109/IJCNN54540.2023.10191341