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Erschienen in:
2020 | OriginalPaper | Buchkapitel
Weibo Rumor Detection Method Based on User and Content Relationship
verfasst von : Zhongyue Zhou, Haijun Zhang, Weimin Pan
Erschienen in: Artificial Intelligence in China
Verlag: Springer Singapore
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Abstract
In order to effectively identify the rumor information in the Weibo platform, we propose a combined model based on deep learning, which includes convolutional neural network (CNN) that incorporates the attention mechanism and combines with the neural network of long short-term memory (LSTM) to implement a microblog rumor detection method for the characteristics of user-content relations. Firstly, the convolutional neural network incorporating the attention mechanism is used to extract the fine-grained features of the user-content relationship. Secondly, the LSTM network is used for coarse-grained feature extraction. Finally, the extracted feature vectors are classified by the Softmax classifier so as to achieve a good effect of rumor detection.