Spotting fake reviewer groups in consumer reviews

By Arjun Mukherjee, B. Liu, Natalie S. Glance. The Web Conference, 2012

829 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/9a331d68b3c559f1831be3fce5c35623616e04e6

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Citations in the corpus

Fake It Till You Make It: Reputation, Competition, and Yelp Review Fraud (2016, neutral): “Our work contributes to the literature on review fraud by developing a method that uses an imperfect indicator of fake reviews to empirically identify the circumstances under which fraud is prevalent.”
Opinion Fraud Detection in Online Reviews by Network Effects (2013, methodology): “Other work has computed scores based on behavioral heuristics, such as rating deviation by (Lim et al. 2010), and frequent itemset mining to find fraudulent reviewer groups by (Mukherjee, Liu, and Glance 2012).”
Exploiting Burstiness in Reviews for Review Spammer Detection (2021, background): “In (Mukherjee, Liu, and Glance 2012), the authors studied the problem of detecting fake reviewer groups by considering both group and individual reviewer behavioral features.”
Collective Opinion Spam Detection: Bridging Review Networks and Metadata (2015, methodology): “Earlier work have used metadata to design features that are indicative of spam [9, 13, 19, 22, 28].”
What Online Reviewer Behaviors Really Matter? Effects of Verbal and Nonverbal Behaviors on Detection of Fake Online Reviews (2016, background): “One is that most fake reviewers are paid to write fake reviews [44].”
A survey on opinion mining and sentiment analysis: Tasks, approaches and applications (2015, neutral): “involved pattern mining based method [239].”
How Community Feedback Shapes User Behavior (2014, background): “…in social media systems is a growing concern (Heymann, Koutrika, and Garcia-Molina 2007), as emphasized by work on review spamming (Lim et al. 2010; Mukherjee, Liu, and Glance 2012; Ott, Cardie, and Hancock 2012), trolling (Shachaf and Hara 2010), social deviance (Shores et al. 2014) and online…”
Sentiment analysis using product review data (2015, background): “Some spam are meaningless at all, while others have irrelevant opinions also known as fake opinions [10-12].”
AR-miner: mining informative reviews for developers from mobile app marketplace (2014, background): “Iacob et al. [28] developed a prototype named MARA that uses a list of linguistic rules to automatically retrieve feature requests from online user reviews.”
External validity of sentiment mining reports: Can current methods identify demographic biases, event biases, and manipulation of reviews? (2014, background): “The articles found for manipulation are given here [24,25,36,80–87].”
Sentiment analysis: A review and comparative analysis of web services (2015, background): “, genre or authorship detection tries to determine the genre or the person who has written a text/opinion [22,78,56,79] or opinion spam detection tries to detect opinions or reviews which contain untrusted contents published to distort public opinion towards people, companies or products [34,65,60,9”