Abstract
Social networks, as an indispensable part of our daily lives, provide ideal platforms for entertainment and communication. However, the appearance of spammers who spread malicious information pollutes a network’s reliability. Unlike email spammers detection, a social network account has several types of attributes and complicated behavior patterns, which require a more sophisticated detection mechanism. To address the above challenges, we propose several efficient profiles and behavioral features to describe a social network account and a combined neural network to detect the spammers. The combined neural network can process the features separately based on their mutual correlation and handle data with missing features. In experiments, the combined neural network outperforms several classical machine learning approaches and achieves \(97.5\%\) accuracy on real data. The proposed features and the combined neural network have already been applied commercially.
W. Pei and Y. Xie have contributed equally to this work.
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Pei, W., Xie, Y., Tang, G. (2018). Spammer Detection via Combined Neural Network. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2018. Lecture Notes in Computer Science(), vol 10934. Springer, Cham. https://doi.org/10.1007/978-3-319-96136-1_28
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DOI: https://doi.org/10.1007/978-3-319-96136-1_28
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