Earticle

초록

영어

The detection of products with manipulated reviews has received widespread research attention, given that a truthful, informative, and useful review helps to significantly lower the search effort and cost for potential customers. This study proposes a method to recognize products with manipulated online customer reviews by examining the sequence of each review’s sentiment, readability, and rating scores by product on randomness, considering the example of a Russian online retail site. Additionally, this study aims to examine the association between brand awareness and existing manipulation with products’ reviews. Therefore, we investigated the difference between well-known and unknown brands’ products online reviews with and without manipulated reviews based on the average star rating and the extremely positive sentiment scores. Consequently, machine learning techniques for predicting products are tested with manipulated reviews to determine a more useful one. It was found that about 20% of all product reviews are manipulated. Among the products with manipulated reviews, 44% are products of well-known brands, and 56% from unknown brands, with the highest prediction performance on deep neural network.

목차

ABSTRACT
Ⅰ. Introduction
Ⅱ. Literature Review
2.1. Brand Awareness
2.2. Manipulation of Online Customer Reviews
2.3. Sentiment and Readability Analysis
2.4. Machine Learning Methods
Ⅲ. Research Framework
3.1. Research Framework
3.2. Research Questions
Ⅳ. Experiments
4.1. Conducting a Survey on Brand Awareness
4.2. Phase 1: Data Collection Using Web Crawling
4.3. Phase 2: Manipulation Detection of OCRs
4.4. Phase 3: Prediction of Products with Manipulated OCRs
Ⅴ. Analysis and Results
5.1. Manipulation Detection
5.2. Comparison of Well-known and Unknown Brands’ Products
5.3. Prediction of Product with Manipulated OCRs
Ⅵ. Conclusion
6.1. Summary of Findings
6.2. Contributions
6.3. Limitations and Further Research
Acknowledgements

저자

• Olga Chernyaeva [ Ph.D. Student, Pusan National University, Korea ]
• Eunmi Kim [ Researcher, Kookmin Information Technology Research Institute in Kookmin University, Korea ]
• Taeho Hong [ Professor, Management Information Systems at College of Business Administration, Pusan National University, Korea ] Corresponding Author

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