Abstract
With the increasing of anomalous user’s intelligent, it is difficult to detect the anomalous users and messages in microblogging. Most of the studies attempt to detect anomalous users or messages individually nowadays. In this paper, we propose a co-clustering algorithm based on nonnegative matrix tri-factorization to detect anomalous users and messages simultaneously. A bipartite graph between user and message is built to model the homogeneous and heterogeneous interactions, and homogeneous relations as constraints to improve the accuracy of heterogeneous co-clustering algorithm. The experimental results show that the proposed algorithm can detect anomalous users and messages with high accuracy on Sina Weibo dataset.
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Shen, G., Yang, W., Wang, W., Yu, M., Dong, G. (2014). Detecting Anomalies in Microblogging via Nonnegative Matrix Tri-Factorization. In: Huang, H., Liu, T., Zhang, HP., Tang, J. (eds) Social Media Processing. SMP 2014. Communications in Computer and Information Science, vol 489. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45558-6_5
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DOI: https://doi.org/10.1007/978-3-662-45558-6_5
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