Abstract
Online social networks such as Twitter have become a major type of information sources in recent years. However, this new public social media provides new gateways for malicious users to achieve various malicious purposes. In this paper, we introduce an extended trust model for detecting malicious activities in online social networks. The major insight is to conduct a trust propagation process over a novel heterogeneous social graph which is able to model different social activities. We develop two trustworthiness measures and evaluate their performance of detecting malicious activities using a real Twitter data set. The results revealed that the F-1 measure of detecting malicious activities in Twitter can achieve higher than 0.9 using our proposed method.
A preliminary version of this work was presented as a poster at the 2013 IEEE/WIC/ACM International Conference on Web Intelligence (WI 2013).
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Agarwal, M., Zhou, B. (2014). Using Trust Model for Detecting Malicious Activities in Twitter. In: Kennedy, W.G., Agarwal, N., Yang, S.J. (eds) Social Computing, Behavioral-Cultural Modeling and Prediction. SBP 2014. Lecture Notes in Computer Science, vol 8393. Springer, Cham. https://doi.org/10.1007/978-3-319-05579-4_25
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DOI: https://doi.org/10.1007/978-3-319-05579-4_25
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