Deep Recurrent Neural Network and Data Filtering for Rumor Detection on Sina Weibo
Abstract
:1. Introduction
1.1. RNN
1.2. Automatic Rumor Detection
2. Materials and Methods
2.1. Dataset
2.1.1. Sina Weibo dataset
2.1.2. Twitter Dataset
2.2. Methods
2.2.1. Problem Definition
2.2.2. Text Vectorization
2.2.3. DRNN Architecture
3. Results
3.1. Filtering Data by the Followers
3.2. Comparisons with Different Models
3.3. Early Detection
3.4. Extensions to Twitter Dataset
4. Discussion
4.1. Data Filtering
4.2. Sequential Encoding
4.3. Performance of Different Models
4.4. Early Detection
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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# of rumor | 2313 |
# of non-rumor | 2351 |
# of posts | 3,752,459 |
# of users | 2,819,338 |
Post-based features | post time, text, repost count, comment count, etc. |
User-based features | registered time, location, follower count, friend count, post count, etc. |
Layer | Name | Description |
---|---|---|
1 | Input | The input layer accepts x0, x1, …, xt sequentially and the dimension of xi is 3000. |
2 | Norm | This layer normalizes the inputs |
3 | Fully-Conn1 | A fully-connected layer with ReLU activation, transforms the dimension of the data to 800. |
4 | Fully-Conn2 | A fully-connected layer with ReLU activation, transforms the dimension of the data to 256. |
5 | RNN1 | RNN layer, transforms the dimension of the data to 32. |
6 | RNN2 | The second RNN layer, keeps the dimension of the data. |
7 | Fully-Conn3 | A fully-connected layer with Sigmoid activation, transforms the dimension of the data to 1 |
8 | Prob | The output layer, which is the probability of an event being a rumor. |
Interval of followers | [0,100) | [100−300) | [200−500) | [500, ∞) |
Posts | 1,205,463 | 1,109,832 | 1,007,667 | 902,490 |
Percentage of posts | 32% | 30% | 27% | 24% |
Literature | Model | Accuracy | Precision | Recall | F1 |
---|---|---|---|---|---|
[22] | SVM-TS | 0.857 | 0.839 | 0.885 | 0.861 |
GRU-2 | 0.910 | 0.876 | 0.956 | 0.914 | |
[23] | CI | 0.928 | - | - | 0.927 |
Proposed | DSRNN | 0.926 | 0.947 | 0.898 | 0.918 |
DGRU | 0.942 | 0.968 | 0.916 | 0.940 | |
DLSTM | 0.954 | 0.975 | 0.935 | 0.953 |
Interval of followers | [0,80) | [80,250) | [250,700) | (700,∞) | All data |
Accuracy | 0.591 | 0.636 | 0.682 | 0.864 | 0.818 |
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Share and Cite
Xu, Y.; Wang, C.; Dan, Z.; Sun, S.; Dong, F. Deep Recurrent Neural Network and Data Filtering for Rumor Detection on Sina Weibo. Symmetry 2019, 11, 1408. https://doi.org/10.3390/sym11111408
Xu Y, Wang C, Dan Z, Sun S, Dong F. Deep Recurrent Neural Network and Data Filtering for Rumor Detection on Sina Weibo. Symmetry. 2019; 11(11):1408. https://doi.org/10.3390/sym11111408
Chicago/Turabian StyleXu, Yichun, Chen Wang, Zhiping Dan, Shuifa Sun, and Fangmin Dong. 2019. "Deep Recurrent Neural Network and Data Filtering for Rumor Detection on Sina Weibo" Symmetry 11, no. 11: 1408. https://doi.org/10.3390/sym11111408