Using Trust Model for Detecting Malicious Activities in Twitter

  • Mohini Agarwal
  • Bin Zhou
Conference paper

DOI: 10.1007/978-3-319-05579-4_25

Volume 8393 of the book series Lecture Notes in Computer Science (LNCS)
Cite this paper as:
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

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.

Keywords

cybercrime Twitter heterogeneous social graph trust model 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Mohini Agarwal
    • 1
  • Bin Zhou
    • 1
  1. 1.Department of Information SystemsUniversity of MarylandBaltimoreUSA