Security issues of news data dissemination in internet environment
Abstract With the rise of artificial intelligence and the development of social media, people's communication is more convenient and convenient. However, in the Internet environment, the untrue dissemination of news data leads to a large number of problems. Efficient and automatic detection of...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2024-03-01
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| Series: | Journal of Cloud Computing: Advances, Systems and Applications |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13677-024-00632-w |
| _version_ | 1797246995139657728 |
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| author | Kang Song Wenqian Shang Yong Zhang Tong Yi Xuan Wang |
| author_facet | Kang Song Wenqian Shang Yong Zhang Tong Yi Xuan Wang |
| author_sort | Kang Song |
| collection | DOAJ |
| description | Abstract With the rise of artificial intelligence and the development of social media, people's communication is more convenient and convenient. However, in the Internet environment, the untrue dissemination of news data leads to a large number of problems. Efficient and automatic detection of rumors in social platforms hence has become an important research direction in recent years. This paper leverages deep learning methods to mine the changing trend of user features related to rumor events, and designs a rumor detection model called Time Based User Feature Capture Model(TBUFCM). To obtain a new feature vector representing the user's comprehensive features under the current event, the proposed model first recomputes the user feature vector by using feature enhancement function. Then it utilizes GRU(Gate Recurrent Unit, GRU) and CNN(Convolutional Neural Networks, CNN) models to learn the global and local changes of user features, respectively. Finally, the hidden rumor features in the process of rumor propagation can be discovered by user and time information. The experimental results show that TBUFCM outperforms the baseline model, and when there are only 20 forwarded posts, it can also reach an accuracy of 92%. The proposed method can effectively solve the security problem of news data dissemination in the Internet environment. |
| first_indexed | 2024-04-24T19:51:39Z |
| format | Article |
| id | doaj.art-613b04cd1dd44ff2abde43720c3fb71a |
| institution | Directory Open Access Journal |
| issn | 2192-113X |
| language | English |
| last_indexed | 2024-04-24T19:51:39Z |
| publishDate | 2024-03-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of Cloud Computing: Advances, Systems and Applications |
| spelling | doaj.art-613b04cd1dd44ff2abde43720c3fb71a2024-03-24T12:33:59ZengSpringerOpenJournal of Cloud Computing: Advances, Systems and Applications2192-113X2024-03-0113111310.1186/s13677-024-00632-wSecurity issues of news data dissemination in internet environmentKang Song0Wenqian Shang1Yong Zhang2Tong Yi3Xuan Wang4State Key Laboratory of Media Convergence and Communication, Communication University of ChinaState Key Laboratory of Media Convergence and Communication, Communication University of ChinaSD steel Rizhao Co.School of Computer Science and Engineering, Guangxi Normal UniversityState Key Laboratory of Media Convergence and Communication, Communication University of ChinaAbstract With the rise of artificial intelligence and the development of social media, people's communication is more convenient and convenient. However, in the Internet environment, the untrue dissemination of news data leads to a large number of problems. Efficient and automatic detection of rumors in social platforms hence has become an important research direction in recent years. This paper leverages deep learning methods to mine the changing trend of user features related to rumor events, and designs a rumor detection model called Time Based User Feature Capture Model(TBUFCM). To obtain a new feature vector representing the user's comprehensive features under the current event, the proposed model first recomputes the user feature vector by using feature enhancement function. Then it utilizes GRU(Gate Recurrent Unit, GRU) and CNN(Convolutional Neural Networks, CNN) models to learn the global and local changes of user features, respectively. Finally, the hidden rumor features in the process of rumor propagation can be discovered by user and time information. The experimental results show that TBUFCM outperforms the baseline model, and when there are only 20 forwarded posts, it can also reach an accuracy of 92%. The proposed method can effectively solve the security problem of news data dissemination in the Internet environment.https://doi.org/10.1186/s13677-024-00632-wDeep learningRumor detectionUser characteristicsCNNGRU |
| spellingShingle | Kang Song Wenqian Shang Yong Zhang Tong Yi Xuan Wang Security issues of news data dissemination in internet environment Journal of Cloud Computing: Advances, Systems and Applications Deep learning Rumor detection User characteristics CNN GRU |
| title | Security issues of news data dissemination in internet environment |
| title_full | Security issues of news data dissemination in internet environment |
| title_fullStr | Security issues of news data dissemination in internet environment |
| title_full_unstemmed | Security issues of news data dissemination in internet environment |
| title_short | Security issues of news data dissemination in internet environment |
| title_sort | security issues of news data dissemination in internet environment |
| topic | Deep learning Rumor detection User characteristics CNN GRU |
| url | https://doi.org/10.1186/s13677-024-00632-w |
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