Review Graph Based Online Store Review Spammer Detection

By Guan Wang, Sihong Xie, B. Liu, Philip S. Yu. 2011 IEEE 11th International Conference on Data Mining, 2011

427 citations recorded by the source corpus.

Online reviews provide valuable information about products and services to consumers. However, spammers are joining the community trying to mislead readers by writing fake reviews. Previous attempts for spammer detection used reviewers' behaviors, text similarity, linguistics features and rating patterns. Those studies are able to identify certain types of spammers, e.g., those who post many similar reviews about one target entity. However, in reality, there are other kinds of spammers who can manipulate their behaviors to act just like genuine reviewers, and thus cannot be detected by the available techniques. In this paper, we propose a novel concept of a heterogeneous review graph to capture the relationships among reviewers, reviews and stores that the reviewers have reviewed. We explore how interactions between nodes in this graph can reveal the cause of spam and propose an iterative model to identify suspicious reviewers. This is the first time such intricate relationships have been identified for review spam detection. We also develop an effective computation method to quantify the trustiness of reviewers, the honesty of reviews, and the reliability of stores. Different from existing approaches, we don't use review text information. Our model is thus complementary to existing approaches and able to find more difficult and subtle spamming activities, which are agreed upon by human judges after they evaluate our results.

Subfields: Artificial Intelligence, Information Systems, Signal Processing

View Paper: https://www.semanticscholar.org/paper/7ca32395ad3c61bfc0295c9b3714d22bd693b7d3

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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.”
Opinion Fraud Detection in Online Reviews by Network Effects (2013, methodology): “reviews, we follow similar ideas as in (Wang et al. 2011).”
Graph based anomaly detection and description: a survey (2014, methodology): “…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 analysis and network effects to exploit connections among fraudulent reviewers [Akoglu et al., 2013a, Wang et al., 2011a, 2012a].”
Exploiting Burstiness in Reviews for Review Spammer Detection (2021, methodology): “Although (Wang et al. 2011) studied the problem of detecting online store review spammers by considering the relationships of reviewers,”
Spotting fake reviewer groups in consumer reviews (2012, methodology): “[32] used a graph-based method to find fake store reviewers.”
REV2: Fraudulent User Prediction in Rating Platforms (2018, methodology): “Similar to the proposed Rev2 algorithm, [17, 22, 32, 33] develop iterative algorithms that jointly assign scores in the rating networks based on consensus of ratings.”
Misinformation in Social Media: Definition, Manipulation, and Detection (2019, background): “Based on the assumption, a supervised learning algorithm can clamp prediction results of a pair of friends [60; 43; 44; 32].”
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): “Graph-based algorithms are developed to identify dense-blocks or dense subgraphs of users and information in the network [6, 16, 40, 48, 79, 106].”
Towards a General Rule for Identifying Deceptive Opinion Spam (2014, background): “…detection are usually focused on developing supervised learningbased algorithms to help users identify deceptive opinion spam, which are highly dependent upon high-quality gold-standard labeled data (Jindal and Liu, 2008; Jindal et al., 2010; Lim et al., 2010; Wang et al., 2011; Wu et al., 2010).”
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”
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].”
What Online Reviewer Behaviors Really Matter? Effects of Verbal and Nonverbal Behaviors on Detection of Fake Online Reviews (2016, background): “Fake reviews can be either unwarranted positive reviews aiming to promote a product, or unjustified false negative comments on competing products in order to damage their reputations [59].”