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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
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Fatima Haouari et al.
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Tiening Sun et al.
Summary: This article proposes a novel approach called Graph Adversarial Contrastive Learning (GACL) to detect and combat internet rumors. The method utilizes the structural information of rumor propagation trees and employs contrastive learning and adversarial feature transformation to improve the model's robustness and accuracy.
PROCEEDINGS OF THE ACM WEB CONFERENCE 2022 (WWW'22)
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Summary: The propagation tree structure of rumors on social media can provide valuable clues, while rumor verification and stance detection are two related tasks that can support each other. However, stance detection typically requires a large amount of labeled data, which is difficult to obtain in practice.
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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
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Summary: In recent years, there has been growing concern over the spread of false information on social media. Research has shown that while most people value sharing accurate news, the veracity of headlines does not necessarily impact their intentions to share. By shifting attention to accuracy, the quality of news that people share can be improved, challenging the belief that people prioritize partisanship over accuracy.
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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2021)
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