A rising number of people use online reviews to choose if they want to use or buy a service or product. Therefore, approaches for identifying fake reviews are in high request. This paper proposes a hybrid rule-based fact-checking framework based on Answer Set Programming (ASP) and natural language processing. The paper incorporates the behavioral patterns of reviewers combined with the qualitative and quantitative properties/features extracted from the content of their reviews. As a case study, we evaluated the framework using a movie review dataset, consisting of user accounts with their associated reviews, including the review title, content, and the star rating of the movie, to identify reviews that are not trustworthy and labeled them accordingly in the output. This output is then used in the front end of a movie review platform to tag reviews as fake and show their sentiment. The evaluation of the proposed approach showed promising results and high flexibility.

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part of Special Issue "Logic-Based Artificial Intelligence" of open access journal "Algorithms"

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