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.
Preview
Unable to display preview. Download preview PDF.
References
AlKhalifa, H.S., AlEidan, R.M.: An experimental system for measuring the credibility of news content in Twitter. International Journal of Web Information Systems 7(2), 130–151 (2011)
Castillo, C., Mendoza, M., Poblete, B.: Information credibility on twitter. In: Proceedings of the 20th, pp. 675–684 (2011)
Gupta, A., Kumaraguru, P.: Credibility ranking of tweets during high impact events. In: Proceedings of Workshop on Privacy and Security in Online Social Media Ser Psosm (2012)
Kwon, S., Cha, M., Jung, K., Chen, W., Wang, Y.: Prominent features of rumor propagation in online social media. In: 2013 IEEE 13th International Conference on Data Mining (ICDM), pp. 1103–1108 (2013)
Mendoza, M., Poblete, B., Castillo, C.: Twitter under crisis: Can we trust what we rt? In: Proceedings of the First Workshop on Social Media Analytics (2010)
Peterson, W.A., Gist, N.P.: Rumor and public opinion. American Journal of Sociology 57(2), 159–167 (1951)
Qazvinian, V., Rosengren, E., Radev, D.R., Mei, Q.: Rumor has it: Identifying misinformation in microblogs. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 1589–1599. Association for Computational Linguistics (2011)
Ratkiewicz, J., Conover, M.D., Meiss, M., Goncalves, B., Flammini, A., Menczer, F.M.: Detecting and tracking political abuse in social media. In: Proceedings of Icwsm (2011)
Sun, S., Liu, H., He, J., Du, X.: Detecting event rumors on sina weibo automatically. In: Ishikawa, Y., Li, J., Wang, W., Zhang, R., Zhang, W. (eds.) APWeb 2013. LNCS, vol. 7808, pp. 120–131. Springer, Heidelberg (2013)
Takahashi, T., Igata, N.: Rumor detection on twitter. In: 13th International Symposium on Advanced Intelligent Systems (ISIS), 2012 Joint 6th International Conference on Soft Computing and Intelligent Systems (SCIS), pp. 452–457 (2012)
Wu, K., Yang, S., Zhu, K.Q.: False rumors detection on sina weibo by propagation structures. In: IEEE International Conference on Data Engineering, ICDE (2015)
Yang, F., Liu, Y., Yu, X., Yang, M.: Automatic detection of rumor on sina weibo. In: Proceedings of the ACM SIGKDD Workshop on Mining Data Semantics (2012)
Yang, Y.: An evaluation of statistical approaches to text categorization. Information Retrieval 1(1–2), 69–90 (1999)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-319-25207-0_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-25206-3
Online ISBN: 978-3-319-25207-0
eBook Packages: Computer ScienceComputer Science (R0)