Examination of Fake Reviews of Product and Approach to Filter them from Legitimate Reviews

Agnihotri, Devavrat; Ahmed, Suza; Darekar, Dhanashree; Gadkari, Chinmay

doi:10.1109/punecon52575.2021.9686477

2021 IEEE Pune Section International Conference (PuneCon) 2021

DOI: 10.1109/punecon52575.2021.9686477

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Examination of Fake Reviews of Product and Approach to Filter them from Legitimate Reviews

Devavrat Agnihotri

Suza Ahmed

Dhanashree Darekar

et al.

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Prevention of Fake Comments using web3

Nithya,

Varsheni,

Brindha

2023

ITM Web Conf.

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The volume of information on the internet is currently rising dramatically. Social media platforms/e-commerce market place is producing a lot of data, including reviews, comments, and opinions, every day. As there are a number of fake reviews should incorporate Spam detection to produce a genuine opinion. Fake reviews are growing problem in online shopping, and they have a significant impact on consumer’s decision-making. Many people today base their decisions when choosing a product or service on social media opinions. Because so many false or phoney evaluations have been written by businesses or individuals for a variety of reasons, detecting opinion spam is a difficult and time-consuming task. They produce fictitious reviews to deceive users or automated detection systems by elevating or degrading the reputations of their target products in order to elevate or lower them. In this article, we’ll regulate it by leveraging blockchain technology to make the review system more authentic by allowing only legitimate product purchasers to submit evaluations using their account credentials. We use the Ethereum blockchain to authenticate user credentials, and we only permit verified users to purchase things. Also, we only permit customers to leave reviews or comments on products, ensuring that the reviews are accurate.

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Prevention of Fake Comments using web3

Nithya,

Varsheni,

Brindha

2023

ITM Web Conf.

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show abstract

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Examination of Fake Reviews of Product and Approach to Filter them from Legitimate Reviews

Cited by 1 publication

References 13 publications

Prevention of Fake Comments using web3

Prevention of Fake Comments using web3

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