Abstract
Sina Weibo services provide platforms for massive information dissemination and sharing between hundreds of millions of users. The hot topics in this platform attract substantial interest and have enormous potential for business and society. As a result, it has attracted spam teams with malicious intent. In this paper, we study how to detect such opinion spam teams and how do they guide public opinion. Our model is unsupervised and adopts a Bayesian framework to distinguish between spammers and non-spammers. Experiments conducted on a dataset of a Sina Weibo hot topic with a 0.81 F1-measure demonstrate the proposed method’s effectiveness. Through further analysis, we found the phenomenon and some methods of the cooperative hyping among VIP users and spammers (in Sect. 3.2). VIP users are small in number but have a great influence due to they have a large number of followers, so VIP users are responsible for hyping topics so that it attract more attention, then a lot of spammers guide public opinion through a lot of manual postings.
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References
Beutel, A., Akoglu, L., Faloutsos, C.: Graph-based user behavior modeling: from prediction to fraud detection. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2309–2310 (2015)
Chen, H., Liu, J., Lv, Y., Li, M.H., Liu, M., Zheng, Q.: Semi-supervised clue fusion for spammer detection in Sina Weibo. Inf. Fusion 44, 22–32 (2017)
Chen, H., Liu, J., Mi, J.: SpamDia: spammer diagnosis in Sina Weibo microblog. In: EAI International Conference on Mobile Multimedia Communications, pp. 116–120 (2016)
Fakhraei, S., Shashanka, M., Shashanka, M., Getoor, L.: Collective spammer detection in evolving multi-relational social networks. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1769–1778 (2015)
Fei, G., Mukherjee, A., Liu, B., Hsu, M., Castellanos, M., Ghosh, R.: Exploiting burstiness in reviews for review spammer detection (2013)
Feng, S., Banerjee, R., Choi, Y.: Syntactic stylometry for deception detection. In: Meeting of the Association for Computational Linguistics: Short Papers, pp. 171–175 (2012)
Feng, S., Xing, L., Gogar, A., Choi, Y.: Distributional footprints of deceptive product reviews (2012)
Hooi, B., Shin, K., Song, H.A., Beutel, A., Shah, N., Faloutsos, C.: Graph-based fraud detection in the face of camouflage. ACM Trans. Knowl. Discov. Data 11(4), 1–26 (2017)
Jindal, N., Liu, B.: Opinion spam and analysis, pp. 219–230 (2008)
Jindal, N., Liu, B., Lim, E.P.: Finding unusual review patterns using unexpected rules. In: ACM International Conference on Information and Knowledge Management, pp. 1549–1552 (2010)
Li, F., Huang, M., Yang, Y., Zhu, X.: Learning to identify review spam. In: International Joint Conference on Artificial Intelligence, pp. 2488–2493 (2011)
Li, H., et al.: Bimodal distribution and co-bursting in review spam detection. In: International Conference on World Wide Web, pp. 1063–1072 (2017)
Lim, E.P., Nguyen, V.A., Jindal, N., Liu, B., Lauw, H.W.: Detecting product review spammers using rating behaviors. In: ACM International Conference on Information and Knowledge Management, pp. 939–948 (2010)
Liu, Y., Zhang, J., Chen, J., Yin, M., Zhang, W.: Detection of hype groups based on mining maximum frequent itemsets in microblogs. Comput. Eng. Appl. (2017)
Ma, Y., Yan, N., Yan, R., Xue, Y.: Detecting spam on Sina Weibo. In: CCIS-13 (2013)
Mukherjee, A., Liu, B., Glance, N.: Spotting fake reviewer groups in consumer reviews. In: International Conference on World Wide Web, pp. 191–200 (2012)
Mukherjee, A., Liu, B., Wang, J., Glance, N., Jindal, N.: Detecting group review spam. In: International Conference Companion on World Wide Web, pp. 93–94 (2011)
Ott, M., Choi, Y., Cardie, C., Hancock, J.T.: Finding deceptive opinion spam by any stretch of the imagination, vol. 1, pp. 309–319 (2011)
Qiao, Y., Zhang, H., Yu, M., Zhang, Y.: Sina-Weibo spammer detection with GBDT. In: Li, Y., Xiang, G., Lin, H., Wang, M. (eds.) SMP 2016. CCIS, vol. 669, pp. 220–232. Springer, Singapore (2016). https://doi.org/10.1007/978-981-10-2993-6_19
Rayana, S., Akoglu, L.: Collective opinion spam detection: bridging review networks and metadata. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 985–994 (2015)
Wang, G., Xie, S., Liu, B., Yu, P.S.: Review graph based online store review spammer detection. In: IEEE International Conference on Data Mining, pp. 1242–1247 (2011)
Xie, S., Wang, G., Lin, S., Yu, P.S.: Review spam detection via temporal pattern discovery. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 823–831 (2012)
Zhang, J., Liu, Y., Luo, J., Dong, Y.: Hype user detection based on feature analysis in microblog. In: International Conference on Multimedia Information Networking & Security, pp. 140–143 (2013)
Acknowledgements
We would like to acknowledge the support provided by the National Key Research and Development Program of China (2017YFA0700601), the Key Research and Development Program of Shandong Province (2017CXGC0605, 2017CXGC0604, 2018GGX101019, 2016GGX106001, 2016GGX101008, 2016ZDJS01A09), the Natural Science Foundation of Shandong Province for Major Basic Research Projects (No. ZR2017ZB0419), the Young Scholars Program of Shandong University, and the TaiShan Industrial Experts Program of Shandong Province (tscy20150305).
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Guo, Z., Liu, S., Wang, Y., Wang, L., Pan, L., Wu, L. (2019). Detect Cooperative Hyping Among VIP Users and Spammers in Sina Weibo. In: Sun, Y., Lu, T., Xie, X., Gao, L., Fan, H. (eds) Computer Supported Cooperative Work and Social Computing. ChineseCSCW 2018. Communications in Computer and Information Science, vol 917. Springer, Singapore. https://doi.org/10.1007/978-981-13-3044-5_18
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DOI: https://doi.org/10.1007/978-981-13-3044-5_18
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