Finding unusual review patterns using unexpected rules

By Nitin Jindal, B. Liu, Ee-Peng Lim. International Conference on Information and Knowledge Management, 2010

371 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/e0bd8a23c27ba88c27e4bce952669c55373dc62e

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Citations in the corpus

Combating Attacks and Abuse in Large Online Communities (2016, background): “Researchers have been working on detecting and characterizing fake product reviews [138, 114], fake co mments on news sites [64], and astroturf political campaigns on Twitter [180].”
Syntactic Stylometry for Deception Detection (2012, background): “2010)) due to notable dif-ﬁculty in obtaining gold standard labels.”
Serf and turf: crowdturfing for fun and profit (2011, background): “Researchers have been working on detecting and characterizing fake product reviews [22, 17], fake comments on news sites [4], and astroturf political campaigns on Twitter [27].”
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.”
What Yelp Fake Review Filter Might Be Doing? (2013, methodology): “Apart from the works mentioned in the introduction, Jindal et al., (2010) studied unexpected reviewing patterns, Wang et al., (2011) investigated graph-based methods for finding fake store reviewers, and Fei et al., (2013) exploited review burstiness for spammer detection.”
Review Graph Based Online Store Review Spammer Detection (2011, background): “Therefore, we propose a novel heterogeneous graph, referred to as the review graph, to capture relations among reviewers, reviews, and stores.”
Collective Opinion Spam Detection: Bridging Review Networks and Metadata (2015, background): “benign users for psycholinguistic clues of deception, behavioral approaches [7, 9, 10, 13, 15, 18, 28] that utilize the reviewing behaviors of users (e.”
Spotting fake reviewer groups in consumer reviews (2012, methodology): “Amazon dataset: In this research, we used product reviews from Amazon [14], which have also been used in [15, 24].”
Graph based anomaly detection and description: a survey (2014, methodology): “This problem has been approached by three different methodologies, based on (i) behavioral analysis [Feng et al., 2012b, Jindal and Liu, 2008, Jindal et al., 2010, Xie et al., 2012], (ii) language stylometry analysis to spot deception [Feng et al., 2012a, Ott et al., 2011], and (iii) relational…”
Exploiting Burstiness in Reviews for Review Spammer Detection (2021, methodology): “Most existing works focused on analyzing one review or one reviewer at a time, neglecting the potential relationships among multiple reviews or reviewers (Jindal and Liu 2008; Lim et al. 2010; Jindal, Liu, and Lim 2010; Li et al. 2011; Ott et al. 2011).”
Spotting opinion spammers using behavioral footprints (2013, background): “Categories and Subject Descriptors H.1.2 [Information Systems]: Human Factors; J.4 [Computer Applications]: Social and Behavioral Sciences General Terms Algorithms, Experimentation, Theory Keywords Abuse, Opinion Spam, Deceptive and Fake Reviewer Detection”
The Enemy in Your Own Camp: How Well Can We Detect Statistically-Generated Fake Reviews – An Adversarial Study (2016, background): “Indeed, a number of previous works have dealt with the detection of fake reviews (Jindal and Liu, 2007; Badaskar et al., 2008; Mackiewicz, 2008; Jindal et al., 2010; Ott et al., 2011; Fornaciari and Poesio, 2014).”
Review spam detection via temporal pattern discovery (2012, neutral): “In this way, the singleton review spam detection problem is mapped to a abnormally correlated pattern detection problem.”
A Survey of Opinion Mining and Sentiment Analysis (2012, methodology): “Experimental results of both papers [40, 56] using amazon.”
Sentiment Analysis and Opinion Mining (2012, methodology): “The second technique also focused on finding fake reviewers or spammers ( Jindal et al., 2010).”
Opinion Fraud Detection in Online Reviews by Network Effects (2013, background): “(Jindal, Liu, and Lim 2010) finds unexpected rules to highlight anomalies, and (Xie et al. 2012; Feng et al. 2012; Feng, Banerjee, and Choi 2012) respectively study temporal review behavior, rating distributions, and syntactic stylometry.”
Towards a General Rule for Identifying Deceptive Opinion Spam (2014, background): “The ﬁrst relies on the judgements of human annotators (Jindal et al., 2010; Mukherjee et al., 2012).”