Predicting Rumor Retweeting Behavior of Social Media Users in Public Emergencies
| Autor: | Rong Fan, Xuejun Ding, Xiaxia Zhang, Yong Tian, Tian Gan |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
General Computer Science
Computer science 02 engineering and technology Machine learning computer.software_genre Convolutional neural network public emergency 020204 information systems convolutional neural networks 0202 electrical engineering electronic engineering information engineering Feature (machine learning) medicine General Materials Science Social media Behavior prediction business.industry General Engineering Panic Rumor rumor 020201 artificial intelligence & image processing lcsh:Electrical engineering. Electronics. Nuclear engineering Artificial intelligence medicine.symptom business lcsh:TK1-9971 computer |
| Zdroj: | IEEE Access, Vol 8, Pp 87121-87132 (2020) |
| ISSN: | 2169-3536 |
| DOI: | 10.1109/access.2020.2989180 |
| Popis: | Rumors in social media not only affect the health of online social networks, but also reduce the quality of information accessed by social media users. When emergencies occur, the rapid spread of rumors can even trigger mass anxiety and panic. However, the existing studies did not make a clear distinction between rumor and non-rumor information in public emergencies, so that they cannot effectively predict the rumor retweeting behavior. To this end, a model for predicting rumor retweeting behavior is presented based on the convolutional neural networks (CNN) called R-CNN model in the paper. In this model, the rumor retweeting behavior is considered as an important driving force of increasing the depth and breadth of rumor cascades, and four feature vectors are constructed with the historical textual content published by users, consisting of attention to public emergencies, attention to rumors, reaction time and tweeting frequency. To input the quantitative feature vectors for R-CNN, a K-means based core tweets extraction method is proposed to select the right tweets, and the quantitative feature representations are proposed. The predictive capability of the model has been proved by experiments base on two rumor datasets of emergencies crawled from Sina weibo. Experimental results indicate that the prediction accuracy of the model reaches 88%, and it can be improved by 7% on average compared with other models. |
| Databáze: | OpenAIRE |
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