Online review system enables users to submit reviews about the products. However, the openness of Internet and monetary rewards for crowdsourcing tasks stimulate a large number of fraudulent users to write fake reviews and post advertisements to interfere the rank of apps. Existing methods for detecting spam reviews have been successful but they usually aims at e-commerce (e.g. Amazon, eBay) and recommendation (e.g. Yelp, Dianping) systems. Since the behaviors of fraudulent users are complexity and varying across different review platforms, existing methods are not suitable for fraudster detection in online app review system.
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Wang et al. (2019) studied this question.
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