KSII Transactions on Internet and Information Systems (TIIS)

Korean Society for Internet Information (한국인터넷정보학회)
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Finding Rotten Eggs: A Review Spam Detection Model using Diverse Feature Sets

  • Akram, Abubakker Usman (Department of Computer Science, COMSATS Institute of Information Technology) ;
  • Khan, Hikmat Ullah (Department of Computer Science, COMSATS Institute of Information Technology) ;
  • Iqbal, Saqib (Department of Software Engineering, Al-Ain University of Science and Technology) ;
  • Iqbal, Tassawar (Department of Computer Science, COMSATS Institute of Information Technology) ;
  • Munir, Ehsan Ullah (Department of Computer Science, COMSATS Institute of Information Technology) ;
  • Shafi, Dr. Muhammad (Department of Computer Science, Air University)
  • Received : 2018.02.02
  • Accepted : 2018.04.23
  • Published : 2018.10.31
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Abstract

Social media enables customers to share their views, opinions and experiences as product reviews. These product reviews facilitate customers in buying quality products. Due to the significance of online reviews, fake reviews, commonly known as spam reviews are generated to mislead the potential customers in decision-making. To cater this issue, review spam detection has become an active research area. Existing studies carried out for review spam detection have exploited feature engineering approach; however limited number of features are considered. This paper proposes a Feature-Centric Model for Review Spam Detection (FMRSD) to detect spam reviews. The proposed model examines a wide range of feature sets including ratings, sentiments, content, and users. The experimentation reveals that the proposed technique outperforms the baseline and provides better results.

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References

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