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Detecting fake news by enhanced text representation with multi-EDU-structure awareness

EXPERT SYSTEMS WITH APPLICATIONS (2022)

PostCom2DR: Utilizing information from post and comments to detect rumors

Yanjie Yang et al.

Summary: The development of social media has led to both information sharing and the spread of rumors. Researchers have proposed a new model based on posts and comments for rumor detection, which improves accuracy through factors such as reply structure, mutual selection information, and topic drift.

EXPERT SYSTEMS WITH APPLICATIONS (2022)

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Article Computer Science, Information Systems

Convolutional neural network with margin loss for fake news detection

Mohammad Hadi Goldani et al.

Summary: This study proposes a method for detecting fake news using Convolutional Neural Networks with different embedding models, comparing static and non-static word embeddings for evaluation on the ISOT and LIAR datasets. The results show promising performance, outperforming existing methods by 7.9% on ISOT and 2.1% on the LIAR dataset.

INFORMATION PROCESSING & MANAGEMENT (2021)

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Article Computer Science, Artificial Intelligence

SemSeq4FD: Integrating global semantic relationship and local sequential order to enhance text representation for fake news detection

Yuhang Wang et al.

Summary: The spread of fake news has caused significant losses to governments and the public. The SemSeq4FD model, which combines global semantic relation modeling and local context learning, can effectively detect fake news in the early stage.

EXPERT SYSTEMS WITH APPLICATIONS (2021)

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Proceedings Paper Computer Science, Information Systems