Collective Opinion Spam Detection: Bridging Review Networks and Metadata

By Shebuti Rayana, Leman Akoglu. Knowledge Discovery and Data Mining, 2015

747 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/99d67902a1c92f0feeda0e70c97a3f6136894a49

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In Signal Processing

Citations in the corpus

False Information on Web and Social Media: A Survey (2018, methodology): “[79] used loopy belief propagation on the review graph network for identifying fake reviews, extending the idea of FraudEagle from Akoglu et al.”
Rethinking Graph Neural Networks for Anomaly Detection (2022, methodology): “The YelpChi dataset (Rayana & Akoglu, 2015) aims to ﬁnd the anomalous reviews which un-justly promote or demote certain products or businesses on Yelp.com.”
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].”
GANG: Detecting Fraudulent Users in Online Social Networks via Guilt-by-Association on Directed Graphs (2017, methodology): “Using undirected social graphs: Some guilt-by-association methods [5], [6], [7], [8], [9], [10], [11], [12], [13] assume symmetric relationships between nodes.”
Alleviating the Inconsistency Problem of Applying Graph Neural Network to Fraud Detection (2020, methodology): “We utilize the YelpChi spam review dataset [11], along with three other benchmark datasets [2, 7] to study the graph inconsistency problem in the fraud detection task.”
Adversarial Attack and Defense on Graph Data: A Survey (2018, neutral): “Social Network Node/Link PolBlogs [1] 1,490 19,025 2 [145], [23], [8], [120], [19], [140], [22], [105] , [52], [53], [36], [134], [20], [54], [56], [77], [59], [135] Node/Link Facebook [65] [109], [21], [92], [143], [103] Node/Community Google+ [65] 107,614 13,673,453 [108], [109], [103] Node Reddit”
Pick and Choose: A GNN-based Imbalanced Learning Approach for Fraud Detection (2021, background): “For example, in the real-world review dataset YelpChi [34] from Yelp.”
REV2: Fraudulent User Prediction in Rating Platforms (2018, methodology): “• SpEagle [26] incorporates behavior features (user, rating text, and product) into FraudEagle, and the final spam scores of users are used for ranking.”
Fake News: A Survey of Research, Detection Methods, and Opportunities (2018, background): “…for a reviewer among products ✓ ✓ ✓ ✓ Manuscript submitted to ACM (1) Probabilistic Graphical Models (PGMs) are frequently adopted either in directed setting (Bayesian Network, BN) [Hooi et al. 2016; Rayana and Akoglu 2016] or undirected setting (Markov Random Field, MRF) [Rayana and Akoglu 2015].”
Automated Crowdturfing Attacks and Defenses in Online Review Systems (2017, background): “Similar to previous work [45, 48, 56], we treat Yelp filtered and unfiltered reviews as ground-truth information for fake and real reviews.”
Call Attention to Rumors: Deep Attention Based Recurrent Neural Networks for Early Rumor Detection (2017, background): “Embedding social graphs into a classification model also helps distinguish malicious user comments from normal ones [20, 21].”
Graph-based Security and Privacy Analytics via Collective Classification with Joint Weight Learning and Propagation (2018, methodology): “Yelp: We obtained a Yelp dataset from [25], which contains Yelp reviews for restaurants located in New York City.”
Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsters (2020, methodology): “Since the Yelp dataset has imbalanced classes, and we focus more on fraudsters (positive instances), like previous work [29], we utilize ROC-AUC (AUC) and Recall to evaluate the overall performance of all classifiers.”