REV2: Fraudulent User Prediction in Rating Platforms

By Srijan Kumar, Bryan Hooi, Disha Makhija, Mohit Kumar, Christos Faloutsos, Venkatramanan Siva Subrahmanian. Web Search and Data Mining, 2018

455 citations recorded by the source corpus.

Rating platforms enable large-scale collection of user opinion about items(e.g., products or other users). However, untrustworthy users give fraudulent ratings for excessive monetary gains. In this paper, we present REV2, a system to identify such fraudulent users. We propose three interdependent intrinsic quality metrics---fairness of a user, reliability of a rating and goodness of a product. The fairness and reliability quantify the trustworthiness of a user and rating, respectively, and goodness quantifies the quality of a product. Intuitively, a user is fair if it provides reliable scores that are close to the goodness of products. We propose six axioms to establish the interdependency between the scores, and then, formulate a mutually recursive definition that satisfies these axioms. We extend the formulation to address cold start problem and incorporate behavior properties. We develop the REV2 algorithm to calculate these intrinsic quality scores for all users, ratings, and products. We show that this algorithm is guaranteed to converge and has linear time complexity. By conducting extensive experiments on five rating datasets, we show that REV2 outperforms nine existing algorithms in detecting fair and unfair users. We reported the 150 most unfair users in the Flipkart network to their review fraud investigators, and 127 users were identified as being fraudulent(84.6% accuracy). The REV2 algorithm is being deployed at Flipkart.

Subfields: Artificial Intelligence, Information Systems, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/7ed053062375927eb82e4925168f98fea6bc69a0

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Citations in the corpus

Community Interaction and Conflict on the Web (2018, background): “forms has focused primarily on controversies [3, 13, 20, 21, 45, 52] and anti-social behavior, in the form of trolling [11, 12, 42], sockpuppetry [39], harassment and cyberbullying [8, 23, 32, 35, 55, 66, 79], vandalism [43], hate speech [9, 15, 51, 56, 72], and others [17, 24, 40, 41, 44, 46, 54, 6”
Predicting Dynamic Embedding Trajectory in Temporal Interaction Networks (2019, background): “C course is important to develop early intervention measures [23, 46] and predicting when a user is likely to turn malicious on social platforms, like Reddit and Wikipedia, ensures platform integrity [9, 25, 26]. Representation learning, or learning low-dimensional embeddings of entities, is a power”
Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsters (2020, background): “Though previous works have proposed other fraud datasets like Epinions [18] and Bitcoin [40], they only contain graph structures and compacted features, with which we cannot build meaningful multi-relation graphs.”
A Comprehensive Survey on Graph Anomaly Detection With Deep Learning (2021, background): “…chaos with mis-leading beliefs [2], [3], [4], [5], untrustworthy reviews in online review systems can affect customers’ shopping choices [6], [7], [8], network intrusions might leak private personal information to hackers [9], [10], [11], [12], and ﬁnancial frauds can cause huge damage to…”
Rethinking Graph Neural Networks for Anomaly Detection (2022, background): “Nowadays, Graph Neural Networks (GNNs) act as popular approaches for mining structural data, and are naturally applied for the graph anomaly detection task (Bandyopad-hyay et al., 2019; Kumar et al., 2018).”
False Information on Web and Social Media: A Survey (2018, background): “[48] recently showed that fraudulent review writers are typically unfair, in that they give “unreliable” rating scores that differ largely from the product’s average score.”
GMNN: Graph Markov Neural Networks (2019, methodology): “For link classification, we construct two datasets from the Bitcoin Alpha and the Bitcoin OTC datasets (Kumar et al., 2016; 2018) respectively.”