An Explainable Fake News Analysis Method with Stance Information
The high level of technological development has enabled fake news to spread faster than real news in cyberspace, leading to significant impacts on the balance and sustainability of current and future social systems. At present, collecting fake news data and using artificial intelligence to detect fa...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-08-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/12/15/3367 |
| _version_ | 1797586884623335424 |
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| author | Lu Yuan Hao Shen Lei Shi Nanchang Cheng Hangshun Jiang |
| author_facet | Lu Yuan Hao Shen Lei Shi Nanchang Cheng Hangshun Jiang |
| author_sort | Lu Yuan |
| collection | DOAJ |
| description | The high level of technological development has enabled fake news to spread faster than real news in cyberspace, leading to significant impacts on the balance and sustainability of current and future social systems. At present, collecting fake news data and using artificial intelligence to detect fake news have an important impact on building a more sustainable and resilient society. Existing methods for detecting fake news have two main limitations: they focus only on the classification of news authenticity, neglecting the semantics between stance information and news authenticity. No cognitive-related information is involved, and there are not enough data on stance classification and news true-false classification for the study. Therefore, we propose a fake news analysis method based on stance information for explainable fake news detection. To make better use of news data, we construct a fake news dataset built on cognitive information. The dataset primarily consists of stance labels, along with true-false labels. We also introduce stance information to further improve news falsity analysis. To better explain the relationship between fake news and stance, we use propensity score matching for causal inference to calculate the correlation between stance information and true-false classification. The experiment result shows that the propensity score matching for causal inference yielded a negative correlation between stance consistency and fake news classification. |
| first_indexed | 2024-03-11T00:28:30Z |
| format | Article |
| id | doaj.art-7dbebe97c9ae436285f2c32cdc8e626f |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-11T00:28:30Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-7dbebe97c9ae436285f2c32cdc8e626f2023-11-18T22:50:01ZengMDPI AGElectronics2079-92922023-08-011215336710.3390/electronics12153367An Explainable Fake News Analysis Method with Stance InformationLu Yuan0Hao Shen1Lei Shi2Nanchang Cheng3Hangshun Jiang4School of Data Science and Media Intelligence, Communication University of China, Beijing 100024, ChinaState Key Laboratory of Media Convergence and Communication, Communication University of China, Beijing 100024, ChinaState Key Laboratory of Media Convergence and Communication, Communication University of China, Beijing 100024, ChinaState Key Laboratory of Media Convergence and Communication, Communication University of China, Beijing 100024, ChinaState Key Laboratory of Media Convergence and Communication, Communication University of China, Beijing 100024, ChinaThe high level of technological development has enabled fake news to spread faster than real news in cyberspace, leading to significant impacts on the balance and sustainability of current and future social systems. At present, collecting fake news data and using artificial intelligence to detect fake news have an important impact on building a more sustainable and resilient society. Existing methods for detecting fake news have two main limitations: they focus only on the classification of news authenticity, neglecting the semantics between stance information and news authenticity. No cognitive-related information is involved, and there are not enough data on stance classification and news true-false classification for the study. Therefore, we propose a fake news analysis method based on stance information for explainable fake news detection. To make better use of news data, we construct a fake news dataset built on cognitive information. The dataset primarily consists of stance labels, along with true-false labels. We also introduce stance information to further improve news falsity analysis. To better explain the relationship between fake news and stance, we use propensity score matching for causal inference to calculate the correlation between stance information and true-false classification. The experiment result shows that the propensity score matching for causal inference yielded a negative correlation between stance consistency and fake news classification.https://www.mdpi.com/2079-9292/12/15/3367stance informationfake news analysisexplainable AI systemPSM |
| spellingShingle | Lu Yuan Hao Shen Lei Shi Nanchang Cheng Hangshun Jiang An Explainable Fake News Analysis Method with Stance Information Electronics stance information fake news analysis explainable AI system PSM |
| title | An Explainable Fake News Analysis Method with Stance Information |
| title_full | An Explainable Fake News Analysis Method with Stance Information |
| title_fullStr | An Explainable Fake News Analysis Method with Stance Information |
| title_full_unstemmed | An Explainable Fake News Analysis Method with Stance Information |
| title_short | An Explainable Fake News Analysis Method with Stance Information |
| title_sort | explainable fake news analysis method with stance information |
| topic | stance information fake news analysis explainable AI system PSM |
| url | https://www.mdpi.com/2079-9292/12/15/3367 |
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