Corpus ID: 9734683

Combating Fraud in Online Social Networks: Characterizing and Detecting Facebook Like Farms

@article{Ikram2015CombatingFI,
  title={Combating Fraud in Online Social Networks: Characterizing and Detecting Facebook Like Farms},
  author={Muhammad Ikram and Lucky Onwuzurike and Shehroze Farooqi and Emiliano De Cristofaro and A. Friedman and G. Jourjon and Mohammad Ali Kaafar and M. Shafiq},
  journal={arXiv: Social and Information Networks},
  year={2015}
}
  • Muhammad Ikram, Lucky Onwuzurike, +5 authors M. Shafiq
  • Published 2015
  • Computer Science
  • arXiv: Social and Information Networks
  • As businesses increasingly rely on social networking sites to engage with their customers, it is crucial to understand and counter reputation manipulation activities, including fraudulently boosting the number of Facebook page likes using so-called like farms. Thus, social network operators have started to deploy various fraud detection algorithms such as graph clustering methods, however, with limited efficacy. In fact, this paper presents a comprehensive analysis and evaluation of existing… CONTINUE READING
    1 Citations

    References

    SHOWING 1-10 OF 42 REFERENCES
    Detecting spammers on social networks
    • 733
    • PDF
    Uncovering Large Groups of Active Malicious Accounts in Online Social Networks
    • 183
    • Highly Influential
    • PDF
    Paying for Likes?: Understanding Facebook Like Fraud Using Honeypots
    • 98
    • PDF
    Towards Detecting Anomalous User Behavior in Online Social Networks
    • 208
    • PDF
    CopyCatch: stopping group attacks by spotting lockstep behavior in social networks
    • 246
    • Highly Influential
    • PDF
    Detecting and characterizing social spam campaigns
    • 199
    • PDF
    Uncovering social spammers: social honeypots + machine learning
    • 625
    • PDF
    Trafficking Fraudulent Accounts: The Role of the Underground Market in Twitter Spam and Abuse
    • 226
    • PDF