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Evaluation of AI Techniques for Detecting Deceptive Reviews in Cyberspace: A Study of Pre- and Post-COVID-19 Trends
2nd International Conference on Electronics and Renewable Systems, ICEARS 2023 ; : 961-967, 2023.
Article in English | Scopus | ID: covidwho-2303023
ABSTRACT
With cyberspace's continuous evolution, online reviews play a crucial role in determining business success in various sectors, ranging from restaurants and hotels to e-commerce applications. Typically, a favorable review for a specific product draws in more consumers and results in a significant boost in sales. Unfortunately, a few businesses are using deceptive methods to improve their online reputation by using fake reviews of competitors. As a result, detecting fake reviews has become a difficult and ever-changing research field. Verbal characteristics extracted from review text, as well as nonverbal features such as the reviewer's engagement metrics, the IP address of the device, and so on, play an important role in detecting fake reviews. This article examines and compares various machine learning techniques for detecting deceptive reviews on various online platforms such as e-commerce websites such as Amazon and online review websites such as Yelp, among others. © 2023 IEEE.
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Full text: Available Collection: Databases of international organizations Database: Scopus Type of study: Experimental Studies Topics: Long Covid Language: English Journal: 2nd International Conference on Electronics and Renewable Systems, ICEARS 2023 Year: 2023 Document Type: Article

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Full text: Available Collection: Databases of international organizations Database: Scopus Type of study: Experimental Studies Topics: Long Covid Language: English Journal: 2nd International Conference on Electronics and Renewable Systems, ICEARS 2023 Year: 2023 Document Type: Article