Deep Recurrent Neural Network and Data Filtering for Rumor Detection on Sina Weibo
Social media makes it easy for individuals to publish and consume news, but it also facilitates the spread of rumors. This paper proposes a novel deep recurrent neural model with a symmetrical network architecture for automatic rumor detection in social media such as Sina Weibo, which shows better p...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2019-11-01
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| Series: | Symmetry |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-8994/11/11/1408 |
| _version_ | 1798039571003342848 |
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| author | Yichun Xu Chen Wang Zhiping Dan Shuifa Sun Fangmin Dong |
| author_facet | Yichun Xu Chen Wang Zhiping Dan Shuifa Sun Fangmin Dong |
| author_sort | Yichun Xu |
| collection | DOAJ |
| description | Social media makes it easy for individuals to publish and consume news, but it also facilitates the spread of rumors. This paper proposes a novel deep recurrent neural model with a symmetrical network architecture for automatic rumor detection in social media such as Sina Weibo, which shows better performance than the existing methods. In the data preparing phase, we filter the posts according to the followers of the user. We then use sequential encoding for the posts and multiple embedding layers to get better feature representation, and multiple recurrent neural network layers to capture the dynamic temporal signals characteristic. The experimental results on the Sina Weibo dataset show that: 1. the sequential encoding performs better than the term frequency-inverse document frequency (TF-IDF) or the doc2vec encoding scheme; 2. the model is more accurate when trained on the posts from the users with more followers; and 3. the model achieves superior improvements over the existing works on the accuracy of detection, including the early detection. |
| first_indexed | 2024-04-11T21:55:43Z |
| format | Article |
| id | doaj.art-43f250f71b044f55be45c079bd14616c |
| institution | Directory Open Access Journal |
| issn | 2073-8994 |
| language | English |
| last_indexed | 2024-04-11T21:55:43Z |
| publishDate | 2019-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Symmetry |
| spelling | doaj.art-43f250f71b044f55be45c079bd14616c2022-12-22T04:01:06ZengMDPI AGSymmetry2073-89942019-11-011111140810.3390/sym11111408sym11111408Deep Recurrent Neural Network and Data Filtering for Rumor Detection on Sina WeiboYichun Xu0Chen Wang1Zhiping Dan2Shuifa Sun3Fangmin Dong4College of Computer and Information, China Three Gorges University, Yichang 443002, ChinaCollege of Computer and Information, China Three Gorges University, Yichang 443002, ChinaCollege of Computer and Information, China Three Gorges University, Yichang 443002, ChinaCollege of Computer and Information, China Three Gorges University, Yichang 443002, ChinaCollege of Computer and Information, China Three Gorges University, Yichang 443002, ChinaSocial media makes it easy for individuals to publish and consume news, but it also facilitates the spread of rumors. This paper proposes a novel deep recurrent neural model with a symmetrical network architecture for automatic rumor detection in social media such as Sina Weibo, which shows better performance than the existing methods. In the data preparing phase, we filter the posts according to the followers of the user. We then use sequential encoding for the posts and multiple embedding layers to get better feature representation, and multiple recurrent neural network layers to capture the dynamic temporal signals characteristic. The experimental results on the Sina Weibo dataset show that: 1. the sequential encoding performs better than the term frequency-inverse document frequency (TF-IDF) or the doc2vec encoding scheme; 2. the model is more accurate when trained on the posts from the users with more followers; and 3. the model achieves superior improvements over the existing works on the accuracy of detection, including the early detection.https://www.mdpi.com/2073-8994/11/11/1408rumor detectionsina weibodeep neural networkrecurrent neural network (rnn)long short-term memory (lstm)gated recurrent unit (gru) |
| spellingShingle | Yichun Xu Chen Wang Zhiping Dan Shuifa Sun Fangmin Dong Deep Recurrent Neural Network and Data Filtering for Rumor Detection on Sina Weibo Symmetry rumor detection sina weibo deep neural network recurrent neural network (rnn) long short-term memory (lstm) gated recurrent unit (gru) |
| title | Deep Recurrent Neural Network and Data Filtering for Rumor Detection on Sina Weibo |
| title_full | Deep Recurrent Neural Network and Data Filtering for Rumor Detection on Sina Weibo |
| title_fullStr | Deep Recurrent Neural Network and Data Filtering for Rumor Detection on Sina Weibo |
| title_full_unstemmed | Deep Recurrent Neural Network and Data Filtering for Rumor Detection on Sina Weibo |
| title_short | Deep Recurrent Neural Network and Data Filtering for Rumor Detection on Sina Weibo |
| title_sort | deep recurrent neural network and data filtering for rumor detection on sina weibo |
| topic | rumor detection sina weibo deep neural network recurrent neural network (rnn) long short-term memory (lstm) gated recurrent unit (gru) |
| url | https://www.mdpi.com/2073-8994/11/11/1408 |
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