Abstract
The rumor detection problem on social network has attracted considerable attention in recent years. Most previous works focused on detecting rumors by shallow features of messages, including content and blogger features. But such shallow features cannot distinguish between rumor messages and normal messages in many cases. Therefore, in this paper we propose an automatic rumor detection method based on the combination of new proposed implicit features and shallow features of the messages. The proposed implicit features include popularity orientation, internal and external consistency, sentiment polarity and opinion of comments, social influence, opinion retweet influence, and match degree of messages. Experiments illustrate that our rumor detection method obtain significant improvement compared with the state-of-the-art approaches. The proposed implicit features are effective in rumor detection on social network.
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© 2015 Springer International Publishing Switzerland
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Zhang, Q., Zhang, S., Dong, J., Xiong, J., Cheng, X. (2015). Automatic Detection of Rumor on Social Network. In: Li, J., Ji, H., Zhao, D., Feng, Y. (eds) Natural Language Processing and Chinese Computing. NLPCC 2015. Lecture Notes in Computer Science(), vol 9362. Springer, Cham. https://doi.org/10.1007/978-3-319-25207-0_10
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DOI: https://doi.org/10.1007/978-3-319-25207-0_10
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