Abstract
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.
Highlights
Using Answer Set Programming.Social media and e-commerce platforms have become a fundamental element of today’s society [1]; as a result, the data on the Web is hugely growing, but the quality of this data requires more investigation [2]
Answer Set Programming (ASP) inherits the advantage of rule-based approaches, (a) flexibility which means that adding new rules or updating the system requires only a few changes compared to other paradigms, (b) it does not require a vast amount of training data, (c) rule-based techniques are considered a white box; it provides traceability and transparency for critical decisions that demand a higher degree of explanation usually provided by machine learning approaches, (d) same rules can be used for another data type, like tweets or Facebook posts, for fact-checking purposes, and (e) clear and understandable specification for users
This paper proposes a dynamic recognition system for detecting fake reviews, spam, and spammers on the Web introducing fake reviews
Summary
Developing fact-checking tools that can detect fake reviews or fake users will help improve the clarity and quality of online data; otherwise, the Web will be a sea of abnormal distribution of rumors and false information. This is not an easy task: researchers in this domain invested a lot of effort, but it still needs to be improved. Activities can be exposed by their behavioral patterns and the other accounts who cooperate with them; for instance, by checking the IP addresses, it is possible to obtain information indicating spamming behaviors, mainly when the same user with the same IP address uses multiple fake accounts Identifying those accounts is highly important and removing them requires well-designed systems, which can identify such strange behaviors and malicious patterns.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
