Review spam detection via temporal pattern discovery

By Sihong Xie, Guan Wang, Shuyang Lin, Philip S. Yu. Knowledge Discovery and Data Mining, 2012

446 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Information Systems

View Paper: https://www.semanticscholar.org/paper/8f0126c7c373138b6c995e6cacf1442e97cd2eec

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Citations in the corpus

Spotting opinion spammers using behavioral footprints (2013, methodology): “For reviewers with fewer reviews, the method in [46] can be applied.”
What Yelp Fake Review Filter Might Be Doing? (2013, background): “, 2012) spammers, and time-series (Xie et al., 2012) and distributional (Feng et al.”
Graph based anomaly detection and description: a survey (2014, methodology): “[Feng et al., 2012b] study the distributional patterns in rating behaviors, while [Xie et al., 2012] focus on temporal reviewing behaviors to detect fake review(er)s.”
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”
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.”
Assisting consumers in detecting fake reviews: The role of identity information disclosure and consensus (2016, background): “The identified characteristics included temporal patterns (Xie et al., 2012), content-related aspects such as word use (Ott et al.”
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].”
Opinion Fraud Detection in Online Reviews by Network Effects (2013, background): “A large body of work on fraud detection relies on review text information (Jindal and Liu 2008; Ott et al. 2011; Feng, Banerjee, and Choi 2012) or behavioral evidence (Lim et al. 2010; Xie et al. 2012; Feng et al. 2012), and ignore the connectivity structure of review data.”
REV2: Fraudulent User Prediction in Rating Platforms (2018, methodology): “Commonly used features are derived from timestamps [21, 35, 37] and review text [5, 27, 29].”
Why people hate your app: making sense of user feedback in a mobile app store (2013, background): “Much work has focused on detecting spam reviews [14, 16, 20, 22, 21].”
False Information on Web and Social Media: A Survey (2018, methodology): “[111] created CAPT-MDTS (Correlated Abnormal Patterns Detection in Multidimensional Time Series) based on burst detection in time-series.”
Exploiting Burstiness in Reviews for Review Spammer Detection (2021, methodology): “There is an existing method that deals with such reviewers (Xie et al. 2012).”