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designer bags cyber mondayFraudulent online sellers often collude with reviewers to garner fake reviews for their products. This act undermines the trust of buyers in product reviews, and potentially reduces the effectiveness of online markets. Being able to accurately detect fake reviews is, therefore, critical. In this study, we investigate several preprocessing and textual-based featuring methods along with machine learning classifiers, including single and ensemble models, to build a fake review detection system. Given the nature of product review data, where the number of fake reviews is far less than that of genuine reviews, we look into the results of each class in detail in addition to the overall results. We recognise from our preliminary analysis that, owing to imbalanced data, there is a high imbalance between the accuracies for different classes (e.g., 1.3% for the fake review class and 99.7% for the genuine review class), despite the overall accuracy looking promising (around 89.7%). We propose two dynamic random sampling techniques that are possible for textual-based featuring methods to solve this class imbalance problem. Our results indicate that both sampling techniques can improve the accuracy of the fake review class-for balanced datasets, the accuracies can be improved to a maximum of 84.5% and 75.6% for random under and over-sampling, respectively. However, the accuracies for genuine reviews decrease to 75% and 58.8% for random under and over-sampling, respectively. We also discover that, for smaller datasets, the Adaptive Boosting ensemble model outperforms other single classifiers; whereas, for larger datasets, the performance improvement from ensemble models is insignificant compared to the best results obtained by single classifiers.
Fig. 5
Akram AU, Khan HU, Iqbal S, Iqbal T, Munir EU, Shafi M (2018) Finding rotten eggs: a review spam detection model using diverse feature sets. KSII Trans Internet Inform Syst 12(10):5120–5142. https://doi.org/10.3837/tiis.2018.10.026
Hernández Fusilier D, Montes-y-Gómez M, Rosso P, Guzmán Cabrera R (2015) Detecting positive and negative deceptive opinions using PU-learning. Inf Process Manag 51(4):433–443. https://doi.org/10.1016/j.ipm.2014.11.001
Hu Z, Chiong R, Pranata I, Bao Y, Lin Y (2019) Malicious web domain identification using online credibility and performance data by considering the class imbalance issue. Ind Manag Data Syst 119(3):676–696. https://doi.org/10.1108/IMDS-02-2018-0072
Article Google Scholar
Luca M, Zervas G (2016) Fake it till you make it: reputation, competition, and yelp review fraud. Manag Sci 62(12):3412–3427. https://doi.org/10.1287/mnsc.2015.2304
Salehan M, Kim DJ (2016) Predicting the performance of online consumer reviews: a sentiment mining approach to big data analytics. Decis Support Syst 81:30–40. https://doi.org/10.1016/j.dss.2015.10.006
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if you prefer to contact us by telephone, please call us today at 800-416-3497 (usa and canada)
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mygenealogist.com
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