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2024 Volume 39
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REVIEW   Open Access    

Recent state-of-the-art of fake review detection: a comprehensive review

  • Keshav Mahavidyalaya, University of Delhi, New Delhi, India

  • Faculty of Engineering & Technology, Manav Rachna International Institute of Research and Studies, Faridabad, India

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  • Abstract: Online reviews have a significant impact on the purchasing decisions of potential consumers. Positive reviews often sway buyers, even when faced with higher prices. This phenomenon has given rise to a deceptive industry dedicated to crafting counterfeit reviews. Companies frequently indulge in procuring bulk fake reviews, employing them to tarnish their rivals’ reputations or artificially bolster their credibility. These spurious reviews materialize through automated systems or compensated individuals. Thus, detecting fake reviews is becoming increasingly important due to their deceptive nature, as they are extremely difficult for humans to identify. To address this issue, current work has focused on machine learning and deep learning techniques to identify fake reviews. However, they have several limitations, including a lack of sufficient training data, inconsistency in providing accurate solutions across different datasets, concept drift, and inability to address new methods that evolved to create fake reviews over time. The objective of this review paper is to find the gaps in the existing research in the field of fake review detection and provide future directions. This paper provides the latest, comprehensive overview and analysis of research efforts focusing on various techniques employed so far, distinguishing characteristics utilized, and the existing datasets used.
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  • Cite this article

    Richa Gupta, Vinita Jindal, Indu Kashyap. 2024. Recent state-of-the-art of fake review detection: a comprehensive review. The Knowledge Engineering Review 39(1), doi: 10.1017/S0269888924000067
    Richa Gupta, Vinita Jindal, Indu Kashyap. 2024. Recent state-of-the-art of fake review detection: a comprehensive review. The Knowledge Engineering Review 39(1), doi: 10.1017/S0269888924000067
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REVIEW   Open Access    

Recent state-of-the-art of fake review detection: a comprehensive review

  • Keshav Mahavidyalaya, University of Delhi, New Delhi, India

  • Faculty of Engineering & Technology, Manav Rachna International Institute of Research and Studies, Faridabad, India

  • Received: 09 November 2023
  • Revised: 01 August 2024
  • Accepted: 13 August 2024
  • Published online: 21 November 2024
The Knowledge Engineering Review  39,  2024, 39(1)  |  Cite this article

Abstract: Abstract: Online reviews have a significant impact on the purchasing decisions of potential consumers. Positive reviews often sway buyers, even when faced with higher prices. This phenomenon has given rise to a deceptive industry dedicated to crafting counterfeit reviews. Companies frequently indulge in procuring bulk fake reviews, employing them to tarnish their rivals’ reputations or artificially bolster their credibility. These spurious reviews materialize through automated systems or compensated individuals. Thus, detecting fake reviews is becoming increasingly important due to their deceptive nature, as they are extremely difficult for humans to identify. To address this issue, current work has focused on machine learning and deep learning techniques to identify fake reviews. However, they have several limitations, including a lack of sufficient training data, inconsistency in providing accurate solutions across different datasets, concept drift, and inability to address new methods that evolved to create fake reviews over time. The objective of this review paper is to find the gaps in the existing research in the field of fake review detection and provide future directions. This paper provides the latest, comprehensive overview and analysis of research efforts focusing on various techniques employed so far, distinguishing characteristics utilized, and the existing datasets used.

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    Richa Gupta, Vinita Jindal, Indu Kashyap. 2024. Recent state-of-the-art of fake review detection: a comprehensive review. The Knowledge Engineering Review 39(1), doi: 10.1017/S0269888924000067
    Richa Gupta, Vinita Jindal, Indu Kashyap. 2024. Recent state-of-the-art of fake review detection: a comprehensive review. The Knowledge Engineering Review 39(1), doi: 10.1017/S0269888924000067
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