Abstract
Microblogs open opportunities for social spammers, who are threatening for microblog services and normal users. Therefore, detecting spammers is an essential task in social network mining. However, existing methods are difficult to achieve desired performance in real applications. The underlying causes are the insufficiency of knowledge learned from limited training examples and the differences between data distributions on training and test examples. To address these, in this paper, we present a transfer metric learning method to extract more informative knowledge underlying training instances by similarity learning and transfer this knowledge to test instances using importance sampling in a unified framework. We evaluate the proposed method on real-world data. Results show that our method outperforms many baselines.
Supported by “The Fundamental Theory and Applications of Big Data with Knowledge Engineering” under the National Key Research and Development Program of China with grant number 2016YFB1000903; National Science Foundation of China under Grant Nos. 61532004, 61532015, 61572399, 61672419 and 61672418; Innovative Research Group of the National Natural Science Foundation of China (61721002); Project of China Knowledge Centre for Engineering Science and Technology; Ministry of Education Innovation Research Team No. IRT17R86.
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Chen, H., Liu, J., Lv, Y. (2018). A Transfer Metric Learning Method for Spammer Detection. In: Ganji, M., Rashidi, L., Fung, B., Wang, C. (eds) Trends and Applications in Knowledge Discovery and Data Mining. PAKDD 2018. Lecture Notes in Computer Science(), vol 11154. Springer, Cham. https://doi.org/10.1007/978-3-030-04503-6_18
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DOI: https://doi.org/10.1007/978-3-030-04503-6_18
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