An End-to-End Rumor Detection Model Based on Feature Aggregation
The social network has become the primary medium of rumor propagation. Moreover, manual identification of rumors is extremely time-consuming and laborious. It is crucial to identify rumors automatically. Machine learning technology is widely implemented in the identification and detection of misinfo...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/6659430 |
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| _version_ | 1832554987173969920 |
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| author | Aoshuang Ye Lina Wang Run Wang Wenqi Wang Jianpeng Ke Danlei Wang |
| author_facet | Aoshuang Ye Lina Wang Run Wang Wenqi Wang Jianpeng Ke Danlei Wang |
| author_sort | Aoshuang Ye |
| collection | DOAJ |
| description | The social network has become the primary medium of rumor propagation. Moreover, manual identification of rumors is extremely time-consuming and laborious. It is crucial to identify rumors automatically. Machine learning technology is widely implemented in the identification and detection of misinformation on social networks. However, the traditional machine learning methods profoundly rely on feature engineering and domain knowledge, and the learning ability of temporal features is insufficient. Furthermore, the features used by the deep learning method based on natural language processing are heavily limited. Therefore, it is of great significance and practical value to study the rumor detection method independent of feature engineering and effectively aggregate heterogeneous features to adapt to the complex and variable social network. In this paper, a deep neural network- (DNN-) based feature aggregation modeling method is proposed, which makes full use of the knowledge of propagation pattern feature and text content feature of social network event without feature engineering and domain knowledge. The experimental results show that the feature aggregation model has achieved 94.4% of accuracy as the best performance in recent works. |
| format | Article |
| id | doaj-art-1ebde14e6e264a14b4376b157fe5622f |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-1ebde14e6e264a14b4376b157fe5622f2025-02-03T05:49:51ZengWileyComplexity1076-27871099-05262021-01-01202110.1155/2021/66594306659430An End-to-End Rumor Detection Model Based on Feature AggregationAoshuang Ye0Lina Wang1Run Wang2Wenqi Wang3Jianpeng Ke4Danlei Wang5Key Laboratory of Aerospace Information Security and Trusted Computing, Ministry of Education, Wuhan, Hubei, ChinaKey Laboratory of Aerospace Information Security and Trusted Computing, Ministry of Education, Wuhan, Hubei, ChinaKey Laboratory of Aerospace Information Security and Trusted Computing, Ministry of Education, Wuhan, Hubei, ChinaKey Laboratory of Aerospace Information Security and Trusted Computing, Ministry of Education, Wuhan, Hubei, ChinaKey Laboratory of Aerospace Information Security and Trusted Computing, Ministry of Education, Wuhan, Hubei, ChinaKey Laboratory of Aerospace Information Security and Trusted Computing, Ministry of Education, Wuhan, Hubei, ChinaThe social network has become the primary medium of rumor propagation. Moreover, manual identification of rumors is extremely time-consuming and laborious. It is crucial to identify rumors automatically. Machine learning technology is widely implemented in the identification and detection of misinformation on social networks. However, the traditional machine learning methods profoundly rely on feature engineering and domain knowledge, and the learning ability of temporal features is insufficient. Furthermore, the features used by the deep learning method based on natural language processing are heavily limited. Therefore, it is of great significance and practical value to study the rumor detection method independent of feature engineering and effectively aggregate heterogeneous features to adapt to the complex and variable social network. In this paper, a deep neural network- (DNN-) based feature aggregation modeling method is proposed, which makes full use of the knowledge of propagation pattern feature and text content feature of social network event without feature engineering and domain knowledge. The experimental results show that the feature aggregation model has achieved 94.4% of accuracy as the best performance in recent works.http://dx.doi.org/10.1155/2021/6659430 |
| spellingShingle | Aoshuang Ye Lina Wang Run Wang Wenqi Wang Jianpeng Ke Danlei Wang An End-to-End Rumor Detection Model Based on Feature Aggregation Complexity |
| title | An End-to-End Rumor Detection Model Based on Feature Aggregation |
| title_full | An End-to-End Rumor Detection Model Based on Feature Aggregation |
| title_fullStr | An End-to-End Rumor Detection Model Based on Feature Aggregation |
| title_full_unstemmed | An End-to-End Rumor Detection Model Based on Feature Aggregation |
| title_short | An End-to-End Rumor Detection Model Based on Feature Aggregation |
| title_sort | end to end rumor detection model based on feature aggregation |
| url | http://dx.doi.org/10.1155/2021/6659430 |
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