TIIS (한국인터넷정보학회)
Current Result Document :
| 한글제목(Korean Title) |
Weibo Disaster Rumor Recognition Method Based on Adversarial Training and Stacked Structure |
| 영문제목(English Title) |
Weibo Disaster Rumor Recognition Method Based on Adversarial Training and Stacked Structure |
| 저자(Author) |
Lei Diao
Zhan Tang
Xuchao Guo
Zhao Bai
Shuhan Lu
Lin Li
|
| 원문수록처(Citation) |
VOL 16 NO. 10 PP. 3211 ~ 3229 (2022. 10) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
To solve the problems existing in the process of Weibo disaster rumor recognition, such as lack of corpus, poor text standardization, difficult to learn semantic information, and simple semantic features of disaster rumor text, this paper takes Sina Weibo as the data source, constructs a dataset for Weibo disaster rumor recognition, and proposes a deep learning model BERT_AT_Stacked LSTM for Weibo disaster rumor recognition. First, add adversarial disturbance to the embedding vector of each word to generate adversarial samples to enhance the features of rumor text, and carry out adversarial training to solve the problem that the text features of disaster rumors are relatively single. Second, the BERT part obtains the word-level semantic information of each Weibo text and generates a hidden vector containing sentencelevel feature information. Finally, the hidden complex semantic information of poorlyregulated Weibo texts is learned using a Stacked Long Short-Term Memory (Stacked LSTM) structure. The experimental results show that, compared with other comparative models, the model in this paper has more advantages in recognizing disaster rumors on Weibo, with an F1_Socre of 97.48%, and has been tested on an open general domain dataset, with an F1_Score of 94.59%, indicating that the model has better generalization. |
| 키워드(Keyword) |
Weibo disaster
Rumor recognition
Adversarial training
BERT
Stacked LSTM
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| 파일첨부 |
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