A Bi-GRU-DSA-based social network rumor detection approach
In the rumor detection based on crowd intelligence, the crowd behavior is constructed as a graph model or probability mode. The detection of rumors is achieved through the collaborative utilization of data and knowledge. Aiming at the problems of insufficient feature extraction ability and data redu...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
De Gruyter
2024-03-01
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| Series: | Open Computer Science |
| Subjects: | |
| Online Access: | https://doi.org/10.1515/comp-2023-0114 |
| _version_ | 1797246666197172224 |
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| author | Huang Xiang Liu Yan |
| author_facet | Huang Xiang Liu Yan |
| author_sort | Huang Xiang |
| collection | DOAJ |
| description | In the rumor detection based on crowd intelligence, the crowd behavior is constructed as a graph model or probability mode. The detection of rumors is achieved through the collaborative utilization of data and knowledge. Aiming at the problems of insufficient feature extraction ability and data redundancy of current rumor detection methods based on deep learning model, a social network rumor detection method based on bidirectional gated recurrent unit (Bi-GRU) and double self-attention (DSA) mechanism is suggested. First, a combination of application program interface and third-party crawler approach is used to obtain microblogging data from publicly available fake microblogging information pages, including both rumor and non-rumor information. Second, Bi-GRU is used to capture the tendency of medium- and long-term dependence of data and is flexible enough to deal with variable length input. Finally, the DSA mechanism is introduced to help reduce the redundant information in the dataset, thereby enhancing the model’s efficacy. The results of the experiments indicate that the proposed method outperforms existing advanced methods by at least 0.114, 0.108, 0.064, and 0.085 in terms of accuracy, precision, recall, and F1-scores, respectively. Therefore, the proposed method can significantly enhance the ability of social network rumor detection. |
| first_indexed | 2024-04-24T19:46:25Z |
| format | Article |
| id | doaj.art-8f9760b4c845487f8e6e8a0bebce0dc4 |
| institution | Directory Open Access Journal |
| issn | 2299-1093 |
| language | English |
| last_indexed | 2024-04-24T19:46:25Z |
| publishDate | 2024-03-01 |
| publisher | De Gruyter |
| record_format | Article |
| series | Open Computer Science |
| spelling | doaj.art-8f9760b4c845487f8e6e8a0bebce0dc42024-03-25T07:27:46ZengDe GruyterOpen Computer Science2299-10932024-03-01141pp. 10010710.1515/comp-2023-0114A Bi-GRU-DSA-based social network rumor detection approachHuang Xiang0Liu Yan1School of New Media Technology, Hunan Mass Media Vocational and Technical College, Changsha, Hunan, 410100, ChinaSchool of Software, Hunan Vocational College of Science and Technology, Changsha, Hunan, 410118, ChinaIn the rumor detection based on crowd intelligence, the crowd behavior is constructed as a graph model or probability mode. The detection of rumors is achieved through the collaborative utilization of data and knowledge. Aiming at the problems of insufficient feature extraction ability and data redundancy of current rumor detection methods based on deep learning model, a social network rumor detection method based on bidirectional gated recurrent unit (Bi-GRU) and double self-attention (DSA) mechanism is suggested. First, a combination of application program interface and third-party crawler approach is used to obtain microblogging data from publicly available fake microblogging information pages, including both rumor and non-rumor information. Second, Bi-GRU is used to capture the tendency of medium- and long-term dependence of data and is flexible enough to deal with variable length input. Finally, the DSA mechanism is introduced to help reduce the redundant information in the dataset, thereby enhancing the model’s efficacy. The results of the experiments indicate that the proposed method outperforms existing advanced methods by at least 0.114, 0.108, 0.064, and 0.085 in terms of accuracy, precision, recall, and F1-scores, respectively. Therefore, the proposed method can significantly enhance the ability of social network rumor detection.https://doi.org/10.1515/comp-2023-0114bi-grudeep learningdouble self-attention mechanismrumor detection |
| spellingShingle | Huang Xiang Liu Yan A Bi-GRU-DSA-based social network rumor detection approach Open Computer Science bi-gru deep learning double self-attention mechanism rumor detection |
| title | A Bi-GRU-DSA-based social network rumor detection approach |
| title_full | A Bi-GRU-DSA-based social network rumor detection approach |
| title_fullStr | A Bi-GRU-DSA-based social network rumor detection approach |
| title_full_unstemmed | A Bi-GRU-DSA-based social network rumor detection approach |
| title_short | A Bi-GRU-DSA-based social network rumor detection approach |
| title_sort | bi gru dsa based social network rumor detection approach |
| topic | bi-gru deep learning double self-attention mechanism rumor detection |
| url | https://doi.org/10.1515/comp-2023-0114 |
| work_keys_str_mv | AT huangxiang abigrudsabasedsocialnetworkrumordetectionapproach AT liuyan abigrudsabasedsocialnetworkrumordetectionapproach AT huangxiang bigrudsabasedsocialnetworkrumordetectionapproach AT liuyan bigrudsabasedsocialnetworkrumordetectionapproach |