Spotting opinion spammers using behavioral footprints

By Arjun Mukherjee, Abhinav Kumar, B. Liu, Junhui Wang, Meichun Hsu, Malú Castellanos, Riddhiman Ghosh. Knowledge Discovery and Data Mining, 2013

454 citations recorded by the source corpus.

Subfields: Information Systems, Sociology and Political Science, Signal Processing

View Paper: https://www.semanticscholar.org/paper/e79fdddd8b882a916aa7082f0bbb663aa05a300e

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Citations in the corpus

Automated Crowdturfing Attacks and Defenses in Online Review Systems (2017, background): “, by increasing the number of friends/contacts on the site) and in assisting defenses [10, 19, 20, 31, 47, 71, 75] (e.”
A review of natural language processing techniques for opinion mining systems (2017, background): “In opinion spam detection and usefulness measurement, various related information about reviewers, raters, and products is taken into account together [16, 17, 18, 19, 20, 21, 22].”
Collective Opinion Spam Detection: Bridging Review Networks and Metadata (2015, methodology): “Current approaches can also be grouped as those that detect fake reviews [6, 7, 9, 13, 14, 22, 26, 28], spam users [1, 5, 15, 18, 26], or spam user groups [19, 29].”
REV2: Fraudulent User Prediction in Rating Platforms (2018, background): “• Spamicity [23] is creates each user’s features as its review burstiness and maximum reviews per day.”
Detecting opinion spams and fake news using text classification (2018, background): “Mukherjee et al.(24) built a so-called author spamicity model (ASM) to detect suspicious reviewers based on their behavior footprint.”
Fake News: A Survey of Research, Detection Methods, and Opportunities (2018, background): “…types of information, e.g., online communications [Hancock et al. 2007; Pak and Zhou 2015; Rubin 2010; Zhou et al. 2004b], reviews [Li et al. 2014; Mukherjee et al. 2013b; Ott et al. 2011; Popoola 2018; Shojaee et al. 2013; Zhang et al. 2016], statements [Fuller et al. 2009; Humpherys et al.…”
What Online Reviewer Behaviors Really Matter? Effects of Verbal and Nonverbal Behaviors on Detection of Fake Online Reviews (2016, background): “● Brand features are characteristics specifically related to target products or services that are reviewed, such as price, sales rank, and business/product type [28, 42].”