An Unsupervised Fake News Detection Framework Based on Structural Contrastive Learning
Abstract Recently, fake news detection on social media (SM) has attracted a lot of attention. With the emergence of fake news at a breakneck pace, the massive spread of fake news has had a serious impact in our society. The authenticity of the news is questionable and there exists a necessity for an...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2025-06-01
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| Series: | Cybersecurity |
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| Online Access: | https://doi.org/10.1186/s42400-024-00342-5 |
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| author | Yajie Guo Shujuan Ji Xianwen Fang Dickson K. W. Chiu Ning Cao Hofung Leung |
| author_facet | Yajie Guo Shujuan Ji Xianwen Fang Dickson K. W. Chiu Ning Cao Hofung Leung |
| author_sort | Yajie Guo |
| collection | DOAJ |
| description | Abstract Recently, fake news detection on social media (SM) has attracted a lot of attention. With the emergence of fake news at a breakneck pace, the massive spread of fake news has had a serious impact in our society. The authenticity of the news is questionable and there exists a necessity for an automated tool for the detection. However, most fake news detection methods are mainly supervised, requiring huge amounts of annotated data, which is time-consuming, expensive, and almost impossible with vast new SM volume. To deal with this problem, in this paper, we propose a novel unsupervised fake news detection framework based on structural contrastive learning by combining the propagation structure of news and contrastive learning to achieve unsupervised training. To validate the influence of parameters and our method’s performance, we design experiment sets on public Twitter and Weibo datasets, which validate our approach outperforms current baseline ones and has proper robustness. |
| format | Article |
| id | doaj-art-d86a646dd11f44179bf90e5535dc36d2 |
| institution | Kabale University |
| issn | 2523-3246 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Cybersecurity |
| spelling | doaj-art-d86a646dd11f44179bf90e5535dc36d22025-08-20T03:47:14ZengSpringerOpenCybersecurity2523-32462025-06-018111310.1186/s42400-024-00342-5An Unsupervised Fake News Detection Framework Based on Structural Contrastive LearningYajie Guo0Shujuan Ji1Xianwen Fang2Dickson K. W. Chiu3Ning Cao4Hofung Leung5Key Laboratory for Wisdom Mine Information Technology of Shandong Province, Shandong University of Science and TechnologyKey Laboratory for Wisdom Mine Information Technology of Shandong Province, Shandong University of Science and TechnologyAnhui Province Engineering Laboratory for Big Data Analysis and Early Warning Technology of Coal Mine Safety, Anhui University of Science and TechnologyFaculty of Education, The University of Hong KongKey Laboratory for Wisdom Mine Information Technology of Shandong Province, Shandong University of Science and TechnologyDepartment of Computer Science and Engineering, The Chinese University of Hong KongAbstract Recently, fake news detection on social media (SM) has attracted a lot of attention. With the emergence of fake news at a breakneck pace, the massive spread of fake news has had a serious impact in our society. The authenticity of the news is questionable and there exists a necessity for an automated tool for the detection. However, most fake news detection methods are mainly supervised, requiring huge amounts of annotated data, which is time-consuming, expensive, and almost impossible with vast new SM volume. To deal with this problem, in this paper, we propose a novel unsupervised fake news detection framework based on structural contrastive learning by combining the propagation structure of news and contrastive learning to achieve unsupervised training. To validate the influence of parameters and our method’s performance, we design experiment sets on public Twitter and Weibo datasets, which validate our approach outperforms current baseline ones and has proper robustness.https://doi.org/10.1186/s42400-024-00342-5Fake newsUnsupervised fake news detection methodPropagation structureContrastive learning method |
| spellingShingle | Yajie Guo Shujuan Ji Xianwen Fang Dickson K. W. Chiu Ning Cao Hofung Leung An Unsupervised Fake News Detection Framework Based on Structural Contrastive Learning Cybersecurity Fake news Unsupervised fake news detection method Propagation structure Contrastive learning method |
| title | An Unsupervised Fake News Detection Framework Based on Structural Contrastive Learning |
| title_full | An Unsupervised Fake News Detection Framework Based on Structural Contrastive Learning |
| title_fullStr | An Unsupervised Fake News Detection Framework Based on Structural Contrastive Learning |
| title_full_unstemmed | An Unsupervised Fake News Detection Framework Based on Structural Contrastive Learning |
| title_short | An Unsupervised Fake News Detection Framework Based on Structural Contrastive Learning |
| title_sort | unsupervised fake news detection framework based on structural contrastive learning |
| topic | Fake news Unsupervised fake news detection method Propagation structure Contrastive learning method |
| url | https://doi.org/10.1186/s42400-024-00342-5 |
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