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- Van-Hoang Nguyen
- National University of Singapore, Singapore
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- Kazunari Sugiyama
- Kyoto University, Kyoto, Japan
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- Preslav Nakov
- Qatar Computing Research Institute, HBKU, Doha, Qatar
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- Min-Yen Kan
- National University of Singapore, Singapore
書誌事項
- タイトル別名
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- leveraging social context for fake news detection using graph representation
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説明
<jats:p>We propose Factual News Graph (FANG), a novel graphical social context representation and learning framework for fake news detection. Unlike previous contextual models that have targeted performance, our focus is on representation learning. Compared to transductive models, FANG is scalable in training as it does not have to maintain the social entities involved in the propagation of other news and is efficient at inference time, without the need to reprocess the entire graph. Our experimental results show that FANG is better at capturing the social context into a high-fidelity representation, compared to recent graphical and nongraphical models. In particular, FANG yields significant improvements for the task of fake news detection and is robust in the case of limited training data. We further demonstrate that the representations learned by FANG generalize to related tasks, such as predicting the factuality of reporting of a news medium.</jats:p>
収録刊行物
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- Communications of the ACM
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Communications of the ACM 65 (4), 124-132, 2022-03-19
Association for Computing Machinery (ACM)
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キーワード
- Social and Information Networks (cs.SI)
- FOS: Computer and information sciences
- Computer Science - Machine Learning
- Computer Science - Computation and Language
- Computer Science - Social and Information Networks
- Computer Science - Information Retrieval
- Machine Learning (cs.LG)
- Computation and Language (cs.CL)
- Information Retrieval (cs.IR)
詳細情報 詳細情報について
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- CRID
- 1360021800248524544
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- ISSN
- 15577317
- 00010782
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- データソース種別
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- Crossref
- OpenAIRE
