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Fake Review Classification Using Supervised Machine Learning

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Pattern Recognition. ICPR International Workshops and Challenges (ICPR 2021)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12664))

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Abstract

The revolution of social media has propelled the online community to take advantage of online reviews for not only posting feedback about the products, services, and other issues but also assists individuals to analyze user’s feedback for making purchase decisions, and companies for improving the quality of manufactured goods. However, the propagation of fake reviews has become an alarming issue, as it deceives online users while purchasing and promotes or demotes the reputation of competing brands. In this work, we propose a supervised learning-based technique for the detection of fake reviews from the online textual content. The study employs machine learning classifiers for bifurcating fake and genuine reviews. Experimental results are evaluated against different evaluation measures and the performance of the proposed system is compared with baseline works.

Supported by Natural Sciences and Engineering Council of Canada (NSERC).

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Notes

  1. 1.

    https://www.kaggle.com/uciml/sms-spam-collection-dataset.

  2. 2.

    https://www.kaggle.com/uciml/sms-spam-collection-dataset.

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Authors and Affiliations

  1. Institute of Computing and Information Technology, Gomal University, D.I.Khan (KP), Pakistan

    Hanif Khan, Muhammad Usama Asghar & Muhammad Zubair Asghar

  2. Department of Math and Computer Science, Brandon University, Brandon, MB, Canada

    Gautam Srivastava

  3. School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, India

    Praveen Kumar Reddy Maddikunta & Thippa Reddy Gadekallu

Authors
  1. Hanif Khan
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  2. Muhammad Usama Asghar
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  3. Muhammad Zubair Asghar
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  4. Gautam Srivastava
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  5. Praveen Kumar Reddy Maddikunta
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  6. Thippa Reddy Gadekallu
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Corresponding author

Correspondence to Gautam Srivastava .

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Editors and Affiliations

  1. Dipartimento di Ingegneria dell'Informazione, University of Firenze, Florence, Firenze, Italy

    Alberto Del Bimbo

  2. Dipartimento di Ingegneria “Enzo Ferrari”, Università di Modena e Reggio Emilia, Modena, Italy

    Rita Cucchiara

  3. Department of Computer Science, Boston University, Boston, MA, USA

    Stan Sclaroff

  4. Dipartimento di Matematica e Informatica, University of Catania, Catania, Catania, Italy

    Giovanni Maria Farinella

  5. Cloud & AI, JD.COM, Beijing, China

    Tao Mei

  6. Dipartimento di Ingegneria dell’Informazione, University of Firenze, Firenze, Italy

    Marco Bertini

  7. Computational Sciences Department, National Institute of Astrophysics, Optics and Electronics (INAOE), Tonantzintla, Puebla, Mexico

    Hugo Jair Escalante

  8. Dipartimento di Ingegneria “Enzo Ferrari”, Università di Modena e Reggio Emilia, Modena, Italy

    Roberto Vezzani

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Khan, H., Asghar, M.U., Asghar, M.Z., Srivastava, G., Maddikunta, P.K.R., Gadekallu, T.R. (2021). Fake Review Classification Using Supervised Machine Learning. In: Del Bimbo, A., et al. Pattern Recognition. ICPR International Workshops and Challenges. ICPR 2021. Lecture Notes in Computer Science(), vol 12664. Springer, Cham. https://doi.org/10.1007/978-3-030-68799-1_19

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  • DOI: https://doi.org/10.1007/978-3-030-68799-1_19

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-030-68799-1

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