Abstract
The rapid development of social networks makes it easy for people to communicate online. However, social networks usually suffer from social spammers due to their openness. Spammers deliver information for economic purposes, and they pose threats to the security of social networks. To maintain the long-term running of online social networks, many detection methods are proposed. But current methods normally use high dimension features with supervised learning algorithms to find spammers, resulting in low detection performance. To solve this problem, in this paper, we first apply the Laplacian score method, which is an unsupervised feature selection method, to obtain useful features. Based on the selected features, the semi-supervised ensemble learning is then used to train the detection model. Experimental results on the Twitter dataset show the efficiency of our approach after feature selection. Moreover, the proposed method remains high detection performance in the face of limited labeled data.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Borge-Holthoefer, J., Rivero, A., Moreno, Y.: Locating privileged spreaders on an online social network. Phys. Rev. E 85(6), 066123 (2012)
Hu, X., Tang, X., Liu, H.: Online social spammer detection. In: AAAI, pp. 59–65 (2014)
Guille, A., Hacid, H., Favre, C., Zighed, D.A.: Information diffusion in online social networks: a survey. ACM SIGMOD Rec. 42(2), 17–28 (2013)
Wu, F., Shu, J., Huang, Y., Yuan, Z.: Social spammer and spam message co-detection in microblogging with social context regularization. In: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, pp. 1601–1610. ACM (2015)
Hu, X., Tang, J., Gao, H., Liu, H.: Social spammer detection with sentiment information. In: 2014 IEEE International Conference on Data Mining, pp. 180–189. IEEE (2014)
Zhu, X., Nie, Y., Jin, S., Li, A., Jia, Y.: Spammer detection on online social networks based on logistic regression. In: Xiao, X., Zhang, Z. (eds.) WAIM 2015. LNCS, vol. 9391, pp. 29–40. Springer, Heidelberg (2015). doi:10.1007/978-3-319-23531-8_3
Heymann, P., Koutrika, G., Garcia-Molina, H.: Fighting spam on social web sites: a survey of approaches and future challenges. IEEE Internet Comput. 11(6), 36–45 (2007)
Stringhini, G., Kruegel, C., Vigna, G.: Detecting spammers on social networks. In: Proceedings of the 26th Annual Computer Security Applications Conference, pp. 1–9. ACM (2010)
Ren, Y., Ji, D., Yin, L., Zhang, H.: Finding deceptive opinion spam by correcting the mislabeled instances. Chin. J. Electron. 24(1), 52–57 (2015)
Aggarwal, A., Almeida, J., Kumaraguru, P.: Detection of spam tipping behaviour on foursquare. In: Proceedings of the 22nd International Conference on World Wide Web, pp. 641–648. ACM (2013)
Gao, H., Hu, J., Wilson, C., Li, Z., Chen, Y., Zhao, B.Y.: Detecting and characterizing social spam campaigns. In: Proceedings of the 10th ACM SIGCOMM Conference on Internet Measurement, pp. 35–47. ACM (2010)
Li, Z., Zhang, X., Shen, H., Liang, W., He, Z.: A semi-supervised framework for social spammer detection. In: Cao, T., Lim, E.-P., Zhou, Z.-H., Ho, T.-B., Cheung, D., Motoda, H. (eds.) PAKDD 2015. LNCS (LNAI), vol. 9078, pp. 177–188. Springer, Heidelberg (2015). doi:10.1007/978-3-319-18032-8_14
Lee, K., Caverlee, J., Webb, S.: Uncovering social spammers: social honeypots + machine learning. In: Proceedings of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 435–442. ACM (2010)
Song, J., Lee, S., Kim, J.: Spam filtering in twitter using sender-receiver relationship. In: Sommer, R., Balzarotti, D., Maier, G. (eds.) RAID 2011. LNCS, vol. 6961, pp. 301–317. Springer, Heidelberg (2011). doi:10.1007/978-3-642-23644-0_16
Zhang, Y., Jianguo, L.: Discover millions of fake followers in Weibo. Soc. Netw. Anal. Min. 6(1), 1–15 (2016)
Tan, E., Guo, L., Chen, S., Zhang, X., Zhao, Y.: UNIK: unsupervised social network spam detection. In: Proceedings of the 22nd ACM International Conference on Information and Knowledge Management, pp. 479–488. ACM (2013)
He, X., Cai, D., Niyogi, P.: Laplacian score for feature selection. In: Advances in Neural Information Processing Systems, pp. 507–514 (2005)
Guyon, I., Elisseeff, A.: An introduction to variable, feature selection. J. Mach. Learn. Res. 3, 1157–1182 (2003)
Li, M., Zhou, Z.-H.: Improve computer-aided diagnosis with machine learning techniques using undiagnosed samples. IEEE Trans. Syst. Man Cybern. Part A Syst. Hum. 37(6), 1088–1098 (2007)
Benevenuto, F., Magno, G., Rodrigues, T., Almeida, V.: Detecting spammers on Twitter. In: Collaboration, Electronic Messaging, Anti-abuse and Spam Conference (CEAS), vol. 6, p. 12 (2010)
Acknowledgments
This work is supported by the Basic and Advanced Research Projects in Chongqing under Grant No. cstc2015jcyjA40049, the National Key Basic Research Program of China (973) under Grant No. 2013CB328903, the National Natural Science Foundation of China under Grant Nos. 61472021 and 61602070, the Fundamental Research Fund for the Central Universities under Grant No. 106112014CDJZR095502, and the China Scholarship Council.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing AG
About this paper
Cite this paper
Li, W., Gao, M., Rong, W., Wen, J., Xiong, Q., Ling, B. (2016). LSSL-SSD: Social Spammer Detection with Laplacian Score and Semi-supervised Learning. In: Lehner, F., Fteimi, N. (eds) Knowledge Science, Engineering and Management. KSEM 2016. Lecture Notes in Computer Science(), vol 9983. Springer, Cham. https://doi.org/10.1007/978-3-319-47650-6_35
Download citation
DOI: https://doi.org/10.1007/978-3-319-47650-6_35
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-47649-0
Online ISBN: 978-3-319-47650-6
eBook Packages: Computer ScienceComputer Science (R0)