Detecting and Characterizing Extremist Reviewer Groups in Online Product Reviews
| Autor: | Viresh Gupta, Aayush Aggarwal, Tanmoy Chakraborty |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Social and Information Networks (cs.SI)
FOS: Computer and information sciences Computer Science - Machine Learning Information retrieval Computer science Computer Science - Social and Information Networks 02 engineering and technology Perceptron Witness Task (project management) Machine Learning (cs.LG) Human-Computer Interaction Consistency (negotiation) 020204 information systems Modeling and Simulation 0202 electrical engineering electronic engineering information engineering Feature (machine learning) Task analysis 020201 artificial intelligence & image processing Set (psychology) Classifier (UML) Social Sciences (miscellaneous) |
| DOI: | 10.48550/arxiv.2004.05865 |
| Popis: | Online marketplaces often witness opinion spam in the form of reviews. People are often hired to target specific brands for promoting or impeding them by writing highly positive or negative reviews. This often is done collectively in groups. Although some previous studies attempted to identify and analyze such opinion spam groups, little has been explored to spot those groups who target a brand as a whole, instead of just products. In this paper, we collected reviews from the Amazon product review site and manually labelled a set of 923 candidate reviewer groups. The groups are extracted using frequent itemset mining over brand similarities such that users are clustered together if they have mutually reviewed (products of) a lot of brands. We hypothesize that the nature of the reviewer groups is dependent on 8 features specific to a (group, brand) pair. We develop a feature-based supervised model to classify candidate groups as extremist entities. We run multiple classifiers for the task of classifying a group based on the reviews written by the users of that group, to determine if the group shows signs of extremity. A 3-layer Perceptron based classifier turns out to be the best classifier. We further study the behaviours of such groups in detail to understand the dynamics of brand-level opinion fraud better. These behaviours include consistency in ratings, review sentiment, verified purchase, review dates and helpful votes received on reviews. Surprisingly, we observe that there are a lot of verified reviewers showing extreme sentiment, which on further investigation leads to ways to circumvent existing mechanisms in place to prevent unofficial incentives on Amazon. Comment: 6 figures, 5 tables, Accepted in IEEE Transactions on Computational Social Systems |
| Databáze: | OpenAIRE |
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