Fake Reviews Detection through Machine learning Algorithms: A Systematic Literature Review

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Abstract These days, most people refer to user reviews to purchase an online product. Unfortunately, spammers exploited this situation to post deceptive reviews and mislead consumers either to promote a product with poor quality or to demote a brand and damage its reputation. Among the solutions to this problem is human verification. Unfortunately, the real-time nature of fake reviews make the task more difficult especially on e-commerce platforms. The aim of this paper is to conduct a systematic literature review to analyze proposed solutions by researchers who were working on setting up an automatic and efficient framework to identify fake reviews, unsolved problems in the domain and future research direction. Our findings emphasize the importance of the use of certain features and provide researchers and practitioners with insights on proposed solutions and their limitations. Thus, the findings of the study reveals that most approaches focus on sentiment analysis, opinion mining and especially machine learning (ML) which contributes to the development of more powerful models that can significantly solve the problem and thus enhance further the accuracy and efficiency of detecting fake reviews.

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