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Deep Recurrent Neural Network and Data Filtering for Rumor Detection on Sina Weibo

1
College of Computer and Information, China Three Gorges University, Yichang 443002, China
2
Department of Electrical Engineering and Computer Science, University of Wisconsin-Milwaukee, Milwaukee, WI 53201, USA
*
Authors to whom correspondence should be addressed.
Symmetry 2019, 11(11), 1408; https://doi.org/10.3390/sym11111408
Received: 12 October 2019 / Revised: 2 November 2019 / Accepted: 13 November 2019 / Published: 15 November 2019
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. View Full-Text
Keywords: rumor detection; Sina Weibo; Deep Neural Network; recurrent neural network (RNN); long short-term memory (LSTM); gated recurrent unit (GRU) rumor detection; Sina Weibo; Deep Neural Network; recurrent neural network (RNN); long short-term memory (LSTM); gated recurrent unit (GRU)
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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.

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