Exploiting Burstiness in Reviews for Review Spammer Detection

By Geli Fei, Arjun Mukherjee, B. Liu, Meichun Hsu, Malú Castellanos, Riddhiman Ghosh. International Conference on Web and Social Media, 2021

372 citations recorded by the source corpus.

Online product reviews have become an important source of user opinions. Due to profit or fame, imposters have been writing deceptive or fake reviews to promote and/or to demote some target products or services. Such imposters are called review spammers. In the past few years, several approaches have been proposed to deal with the problem. In this work, we take a different approach, which exploits the burstiness nature of reviews to identify review spammers. Bursts of reviews can be either due to sudden popularity of products or spam attacks. Reviewers and reviews appearing in a burst are often related in the sense that spammers tend to work with other spammers and genuine reviewers tend to appear together with other genuine reviewers. This paves the way for us to build a network of reviewers appearing in different bursts. We then model reviewers and their co-occurrence in bursts as a Markov Random Field (MRF), and employ the Loopy Belief Propagation (LBP) method to infer whether a reviewer is a spammer or not in the graph. We also propose several features and employ feature induced message passing in the LBP framework for network inference. We further propose a novel evaluation method to evaluate the detected spammers automatically using supervised classification of their reviews. Additionally, we employ domain experts to perform a human evaluation of the identified spammers and non-spammers. Both the classification result and human evaluation result show that the proposed method outperforms strong baselines, which demonstrate the effectiveness of the method.

Subfields: Computer Networks and Communications, Information Systems, Signal Processing

View Paper: https://www.semanticscholar.org/paper/19deb0bd92624e0fccfdc030e25eb2f071f4eacd

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Citations in the corpus

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.”
What Online Reviewer Behaviors Really Matter? Effects of Verbal and Nonverbal Behaviors on Detection of Fake Online Reviews (2016, background): “, [13, 19, 28, 30, 32, 42, 49]), which were extracted from review text.”
Automated Crowdturfing Attacks and Defenses in Online Review Systems (2017, background): “cosine similarity between unigram features among all pairs of sentences [10, 20, 31, 56].”
Detecting opinion spams and fake news using text classification (2018, background): “Fei et al.(25) focused on detecting spammers who write reviews in short time windows.”
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): “content similarity—pairwise cosine similarity among user’s (product’s) reviews, where a review is represented as a bag-of-bigrams [5, 15] MCS H Max.”
Graph-based Security and Privacy Analytics via Collective Classification with Joint Weight Learning and Propagation (2018, background): “Burst [41] Fake Review Detection Undirected Both N ICWSM’13”