DETECTION OF FAKE REVIEWS ON SOCIAL MEDIA USING MACHINE LEARNING ALGORITHMS
Issues in Information Systems · 2020
With the development of the Internet, technology and e-commerce, online-purchasing is easier and more convenient these days.Online reviews become the main source of information that customers usually refer to for their purchasing decision.However, many of reviews given by the online users are not considered truthful.Because of commercial benefits, fake reviews were generated to mislead customers.Therefore, it is necessary to detect fake reviews effectively.This paper aims to improve the performance of fake review classifiers by integrating different techniques into classifying models.More specifically, we analyzed similarity between reviews and utilized the EM (Expectation Maximization) clustering algorithm to recognize the review patterns.We also applied the sentiment analysis to analyze the reviews.Using the results from clustering models, sentiment analysis, and non-textual features of reviews and reviewers, we built machine learning models to classify fake reviews.We compare three supervised machine learning algorithms: Support Vector Machine, Artificial Neural Network and Random Forest.The empirical results from our experiments showed that the Random Forest algorithm outperforms against other algorithms.It also proved our assumption about text clustering and non-textual features in fake review detections.