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مقاله انگلیسی ایجاد و تشخیص نقد و بررسی جعلی محصولات آنلاین

این مقاله علمی پژوهشی (ISI)  به زبان انگلیسی از نشریه الزویر مربوط به سال ۲۰۲۲ دارای ۱۵ صفحه انگلیسی با فرمت PDF می باشد در ادامه این صفحه لینک دانلود رایگان مقاله انگلیسی و بخشی از ترجمه فارسی مقاله موجود می باشد.

کد محصول: M1216

سال نشر: ۲۰۲۲

نام ناشر (پایگاه داده): الزویر

نام مجله:   Journal of Retailing and Consumer Services

نوع مقاله: علمی پژوهشی (Research articles)

تعداد صفحه انگلیسی:۱۵ صفحه PDF

عنوان کامل فارسی:

مقاله انگلیسی ۲۰۲۲ :  ایجاد و تشخیص نقد و بررسی جعلی محصولات آنلاین

عنوان کامل انگلیسی:

 Creating and detecting fake reviews of online products

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Abstract

Customers increasingly rely on reviews for product information. However, the usefulness of online reviews is impeded by fake reviews that give an untruthful picture of product quality. Therefore, detection of fake reviews is needed. Unfortunately, so far, automatic detection has only had partial success in this challenging task. In this research, we address the creation and detection of fake reviews. First, we experiment with two language models, ULMFiT and GPT-2, to generate fake product reviews based on an Amazon e-commerce dataset. Using the better model, GPT-2, we create a dataset for a classification task of fake review detection. We show that a machine classifier can accomplish this goal near-perfectly, whereas human raters exhibit significantly lower accuracy and agreement than the tested algorithms. The model was also effective on detected human generated fake reviews. The results imply that, while fake review detection is challenging for humans, “machines can fight machines” in the task of detecting fake reviews. Our findings have implications for consumer protection, defense of firms from unfair competition, and responsibility of review platforms.

Keywords: Fake reviews, Detection e-commerce, eWOM, Marketing

۱.Introduction

 The “phenomenon of fake” is taking over marketing. Major drivers for this are (a) the rapid technological development that enables the creation of artificial consumer-facing outputs, such as deepfakes (Floridi, 2018; Jan et al., 2020; Tolosana et al., 2020), and (b) the marketplace evolving around these artificial outputs, related to fake creation, detection, and mitigation (Hajek and Henriques, 2017). Among the most impactful artificial marketing outputs are fake product reviews — also known as ‘fake reviews,’ ‘deceptive reviews,’ ‘deceptive opinion spam,’ ‘review spam,’ or ‘review fraud’ — that pass as real ones. To this end, studying fake reviews has been suggested as one of the primary agenda items in digital and social media marketing research (Dwivedi et al., 2020). Online product reviews, as a form of electronic Word-of-Mouth (eWOM), are major drivers in influencing consumers’ purchase decisions (Duarte et al., 2018; Endo et al., 2012; Kaushik et al., 2018; Sandra MC Loureiro and Javier Miranda, 2018; Tran and Strutton, 2020). In the United States, more than 80% of consumers indicate they use online reviews before purchasing a product (Smith and Anderson, 2016). As reviews are among the most influential factors on consumers’ buying behavior, fraudulent actors are tempted to hire writers who specialize in or use automated methods for generating fake reviews to enhance the attractiveness of their products and services, or to degrade competitors’ reputation…

۱۰.Conclusion

 Detection of fake reviews is a problem for researchers, e-commerce sites, and firms engaged in online business. Our results indicate that current text generation methods yield fake reviews that appear so realistic that it is challenging for a human to detect them. Fortunately, machine learning classifiers do much better in this regard, with almost perfect accuracy in detecting reviews generated by other machines. This implies that “machines can fight machines” in the battle against fake reviews. Future research is needed for experimenting with more datasets and platforms…

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