[No title available]: A Dynamically Adaptive Defense to a Novel Attack on Review Fraud Detection Engines

Youzhi Zhang, Sayak Chakrabarty, Rui Liu, Andrea Pugliese, V. S. Subrahmanian

Research output: Contribution to journalArticlepeer-review

Abstract

Fake reviews are having a devastating negative influence on online shopping sites. The proliferation of fake reviews is exacerbated by the presence of, companies that create and operate huge sets of sockpuppet accounts to promote their customers’ products by posting fake reviews. Our proposed algorithm allows such companies to optimize their actions to maximize profits. We show that compromises the F1-score of four well-known review fraud detection engines on real-world datasets (up to 27.1% more than baselines). We then propose a defense algorithm called and show that it mitigates the impact of (up to 69.2% with respect to F1-score).

Original languageEnglish (US)
Pages (from-to)1-13
Number of pages13
JournalIEEE Transactions on Computational Social Systems
DOIs
StateAccepted/In press - 2023

Keywords

  • Deep Learning
  • electronic commerce
  • fraud
  • Markov processes

ASJC Scopus subject areas

  • Modeling and Simulation
  • Social Sciences (miscellaneous)
  • Human-Computer Interaction

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