Fake Product Reviews Research Articles

Checking Counterfeit Critiques on Commodities using Ensemble Classifiers Enhancing Information Credibility

The conundrum of the ubiquitous deceptive reviews has overruled the online ontology with the obsession of obscure but obligatory posting of product reviews for the customers to believe, behold and beget the online product marketing. This mandates contemporary research in the direction to delve deeper on the application and analysis of deceiving online reviews with matured and advanced AI models functional on large scale datasets to effectively and efficiently demarcate between the genuine and the sham. The research counteracts the counterfeiting product reviews via the applications, assessment and analysis of the befitting AI models - Elastic-net Classifier model based on block coordinate descent with Wordcloud and its further performance enhancement through LightGBM Trees Classifier with Grid Search and Early Stopping support, with Log-Loss as performance metric for experimentation to gain insight into the intricacies of detection, diagnosis and diminution of fake product reviews. The paper also delineates discriminative and affirmative aspects of the dataset quality, statistics, stability and standards inherent and coherent to the creation of the dataset using Large Language Models (LLMs) intrinsic to the zeitgeist juncture of recent times promoting machines to produce large scale, cost effective bogus reviews in lieu of the Amazon Mechanical Turks. The results obtained with the Log-Loss holdout score of 0.1462 conforming the LightGBM classifier proves its performance better than the Elastic-Net classifier, conforming it as better than the ROC-AUC in terms of its proximity to the prediction probability for the matching actual/true value.

Ecommerce Fake Product Reviews Monitor and Deletion System using Machine Learning Techniques

Ecommerce Fake Product Reviews Monitor and Deletion System using Machine Learning Techniques

Survey on Various Tool for Analyzing and Detecting Fake Review by using AI

The prevalence of fake product reviews in online shopping platforms has raised concerns regarding the authenticity and reliability of customer feedback. This study proposes a method for detecting fake reviews using a logistic regression model and review-centric features. By analyzing linguistic patterns, sentiment analysis, and reviewer behavior, the proposed model aims to accurately differentiate between genuine and fake reviews. Experimental evaluations on real-world datasets demonstrate an overall accuracy of 82%, showcasing the effectiveness of the approach. The integration of natural language processing techniques allows for the extraction of important features from review texts, which are then utilized as inputs to the logistic regression model. Additionally, considering reviewer behavior, such as the number of reviews, consistency of ratings, and timing, enhances the accuracy of the detection system. The proposed method offers a scalable solution that can be easily implemented in existing e-commerce platforms, bolstering their credibility and protecting consumers from misleading information. By filtering out fake reviews, this approach empowers users to make informed purchasing decisions, ultimately improving customer satisfaction in the online marketplace

Leveraging Social Media to Buy Fake Reviews

We study the market for fake product reviews on Amazon.com. Reviews are purchased in large private groups on Facebook and other sites. We hand collect data on these markets and then collect a panel of data on these products' ratings and reviews on Amazon, as well as their sales rank, advertising, and pricing policies. We find that a wide array of products purchase fake reviews, including products with many reviews and high average ratings. Buying fake reviews on Facebook is associated with a significant but short-term increase in average rating and number of reviews. We exploit a sharp but temporary policy shift by Amazon to show that rating manipulation has a large causal effect on sales. Finally, we examine whether rating manipulation harms consumers or whether it is mainly used by high-quality in a manner like advertising or by new products trying to solve the cold-start problem. We find that after firms stop buying fake reviews, their average ratings fall and the share of one-star reviews increases significantly, particularly for young products, indicating rating manipulation is mostly used by low-quality products.

Detection of Fake Product Review using Machine Learning Techniques

Abstract: Online reviews play a crucial role in determining whether a product will be sold on e-commerce websites or applications. Because so many people rely on online reviews, unethical actors may create reviews in order to artificially boost or devalue goods and services. This study presents a semi-supervised machine learning strategy to identify fake product reviews. In addition, this work extracts several reviewer behaviours using feature engineering techniques. In this work, we investigate the findings of several experiments on a genuine food review dataset of restaurant evaluations with features gathered from user behaviour. The results show that Random Forest, with the best f-score of 98%, outperforms another classifier in terms of off-score. The data also shows that accounting for the reviewers' behavioural traits boosts the f-score, and the final accuracy came out at 97.7%. The behaviour of different types of reviewers has not been taken into account in the existing methodology. The effectiveness of the proposed fake review detecting algorithm will be further enhanced by the addition of other low-level data, such as frequent time or date dependency, the timing of the reviewer's delivery of a review, and how typical it is to supply favourable or unfavourable reviews.

MONITORING AND REMOVAL OF FAKE PRODUCT REVIEW USING MACHINE LEARNING (ML)

The employment of Natural Language Processing (NLP) as a means for identifying and eliminating counterfeit products from a dataset is a significant endeavor. The current project employs a Machine Learning (ML) Algorithm to discern fraudulent customer reviews within a given dataset. The algorithm leverages predictive modeling techniques to estimate the authenticity of such reviews, and subsequently assess their validity. The prominence of product reviews as a critical determinant of consumer purchasing behavior in the realm of electronic commerce has led to a concerning escalation in the frequency of counterfeit reviews on websites and applications. It is imperative for the prominent Ecommerce firms to confront the issue of counterfeit product reviews, prior to engaging in the procurement of commodities from established entities. The utilization of this particular approach facilitates the rectification process concerning counterfeit product evaluations, thereby eliminating the prevalence of spammers. This proactive measure works to ensure that there is no potential compromise of trust among users of Ecommerce platforms. Through implementation of this initiative, the enterprise's administration can identify spurious evaluations and subsequently implement appropriate measures directed towards their resolution. The present model has been constructed employing the Naïve Bayes Algorithmic approach. By implementing the algorithm, it is possible to discern spam reviews from those that are legitimate, on websites or applications. To tally the individuals who engage in fraudulent online practices, a dataset is necessary. In our study, the "Amazon Academic Dataset" serves as the fitting reference point to prepare the model and can potentially increase the precision and adaptability of the results.

FAKE PRODUCT REVIEW DETECTION

FAKE PRODUCT REVIEW DETECTION

The Impact of Fake Reviews of Online Goods on Consumers

The fake product reviews on the Internet have brought great obstacles to consumers to make the right purchase decision. Fake reviews reduce the goodwill of e-commerce platforms and harm the interests of most merchants on the platforms. The appearance of fake reviews is easy to mislead consumers to make wrong decisions. Therefore, the research and identification of fake comments are urgent and significant. This paper will detailed discussion and analyze the impact of fake reviews on consumers from the perspective of the formation of consumers' purchase decisions. The four dimensions are demand cognition, looking for alternative plans, purchase decisions, and purchase behavior. Fake reviews may stimulate consumers' purchase desire by changing their demand perception. When evaluating alternative plans, they are affected by the reputation of merchants, etc., and fake reviews significantly affect the purchase decision. Consumers may make positive or negative comments after a purchase. When reviewers do not have expectations for the goods they receive, they become distrustful of the business and the platform, and they give emotionally negative reviews. If satisfied, positive publicity feedback will be given. This paper conducts literature and case studies on the impact of poor information on consumers caused by artificial evaluations and the prevention and control of fake reviews. The author will analyze the reasons for the occurrence of false comments and discuss how to prevent the occurrence of false information to the maximum extent, including the establishment of reward and punishment mechanisms, innovating the detection technology of fake commodity reviews to avoid unfair competition, and strengthening the control of real-name information on the Internet.

A Robust Text Dependent Methodology for Automatic Detection of Product Fake Reviews using TF-IDF

A rise in fake product reviews, which companies employ to attract more customers, has been brought on by the growth of social reviews on the internet. As they only concentrate on linguistic characteristics and miss the semantic meaning of reviews, current models for the detection of fake reviews are limited in their ability to distinguish between fake and genuine reviews. To solve this issue, we provide a brand-new ensemble model that integrates transformer architecture with modelling, TF-IDF models, and false review detection. In addition, our method employs suspicion to spot phone reviewer groups. Our results demonstrate that our suggested method beats state-of-the-art baselines in identifying fraudulent reviewer groups after thorough testing on real-world datasets.

Confounds and overestimations in fake review detection: Experimentally controlling for product-ownership and data-origin.

The popularity of online shopping is steadily increasing. At the same time, fake product reviews are published widely and have the potential to affect consumer purchasing behavior. In response, previous work has developed automated methods utilizing natural language processing approaches to detect fake product reviews. However, studies vary considerably in how well they succeed in detecting deceptive reviews, and the reasons for such differences are unclear. A contributing factor may be the multitude of strategies used to collect data, introducing potential confounds which affect detection performance. Two possible confounds are data-origin (i.e., the dataset is composed of more than one source) and product ownership (i.e., reviews written by individuals who own or do not own the reviewed product). In the present study, we investigate the effect of both confounds for fake review detection. Using an experimental design, we manipulate data-origin, product ownership, review polarity, and veracity. Supervised learning analysis suggests that review veracity (60.26-69.87%) is somewhat detectable but reviews additionally confounded with product-ownership (66.19-74.17%), or with data-origin (84.44-86.94%) are easier to classify. Review veracity is most easily classified if confounded with product-ownership and data-origin combined (87.78-88.12%). These findings are moderated by review polarity. Overall, our findings suggest that detection accuracy may have been overestimated in previous studies, provide possible explanations as to why, and indicate how future studies might be designed to provide less biased estimates of detection accuracy.

Fake Product Review Monitoring and Removal For Genuine Online Shopping

Abstract: In today’s world reviews play a vital role in online shopping and sales of the product because people consider all the pros and cons of any product before they buy it as there are many different options for the same product as well as different manufacturers for the same type of product with the difference in quality. While buying the product the reviews are directly related to the sales of the product and thus it is necessary for online websites to spot fake reviews and remove them. So, Fake Review Detection is used to spot any fraudulent going on because it’s not possible for them to verify every product and sale manually so this application comes into the picture that tries to detect fake reviews given by the customers and remove them for genuine online shopping.

Fake Product Review Detection and Removal System using NLP

Fake review detection and its elimination from the given data set using different natural language processing techniques is important in several aspects fake review dataset is trained by applying two different machine learning models to predict the accuracy of how genuine are the reviews in a given data set. The fake review problem must be addressed so that these large ecommerce industries such as amazon, Flipkart, etc.

The Market for Fake Reviews

This paper studies the market for fake product reviews on Amazon.com.

Detecting and Analysing Fake Opinions Using Artificial Intelligence Algorithms

In e-commerce and on social media, identifying fake opinions has become a tremendous challenge. Such opinions are widely generated on the internet by fake viewers, also called fraudsters. They write deceptive reviews that purport to reflect actual user experience either to promote some products or to defame others. They also target the reputations of e-businesses. Their aim is to mislead customers to make a wrong purchase decision by selecting undesired products. Such reviewers are often paid by rival e-business companies to compose positive reviews of their products and/or negative reviews of other companies’ products. The main objective of this paper is to detect, analyze and calculate the difference between fake and truthful product reviews. To do this, the methodology has planned to have seven phases: reviewing online products, analyzing features through linguistic enquiry and word count (LIWC), preprocessing the data to clean and normalize them, embedding words (Word2Vec) and analyzing performance using artificial deep-learning algorithms for classifying fake and truthful reviews. Two deep-learning neural network models have been evaluated based on standard Yelp product reviews. These models are bidirectional long-short term memory (BiLSTM) and convolutional neural network (CNN). The results from comparing the performance of the two models showed that the BiLSTM model provided higher accuracy for detecting fake reviews than the CNN model.

Exploiting social media for fake reviews

We provide an overview of our recent work that studies the market for fake product reviews on Amazon.com where reviews are purchased in large private internet groups on Facebook and other sites. We find that a wide array of products purchase fake reviews, including products with many reviews and high average ratings. Buying fake reviews on Facebook is associated with a significant but short-term increase in average rating and number of reviews. We exploit a sharp but temporary policy shift by Amazon to show that rating manipulation has a large causal effect on sales. Finally, we examine whether rating manipulation harms consumers or whether it is mostly used by high-quality or young products in a manner akin to advertising. We find that after firms stop buying fake reviews, their average ratings fall and the share of one-star reviews increases significantly, particularly for young products, indicating rating manipulation is mostly used by low-quality products and is deceiving and harming consumers.

Fuzzy artificial bee colony‐based CNN‐LSTM and semantic feature for fake product review classification

SummaryIn recent years, online reviews are considered as the most significant resource for consumers to make a decision regarding the purchase of a particular product. The reviews can either encourage or relegate a product; therefore posting fake reviews has turned into a money‐spinning business in the modern period. The detection of fake reviews has become a center of attraction for various business people. This research study aims in detecting fake product reviews using four significant phases namely the data pre‐processing, feature extraction, feature selection, and classification. The features obtained in the pre‐processing phase are extracted and selected using chi‐squared technique to obtain a delegate subset among all data and to reduce the complication issues. Then a CNNLSTM‐FABC approach classifies and detects the review as fake or real. Finally, the performance evaluation and the comparative analysis are carried out to determine the effectiveness of the proposed approach. The results reveal that the proposed approach performs well irrespective of the product type and sentiment polarity.

Using a hybrid content-based and behaviour-based featuring approach in a parallel environment to detect fake reviews

Abstract The financial impact of positive reviews has prompted some fraudulent sellers to generate fake product reviews for either promoting their products or discrediting competing products. Many e-commerce portals have implemented measures to detect such fake reviews, and these measures require excellent detectors to be effective. In this work, we propose 133 unique features from the combination of content and behaviour-based features to detect fake reviews using machine learning classifiers. Preliminary results show that these features can provide good results for all datasets tested. Detailed analysis of the results, however, reveals the existence of class imbalance issues for two of the bigger datasets - there is a high imbalance between the accuracies of different classes (e.g., 7.73% for the fake class and 99.3% for the genuine class using a Multilayer Perceptron classifier). We therefore introduce two sampling methods that can improve the accuracy of the fake review class on balanced datasets. The accuracies can be improved to a maximum of 89% for both random under and over-sampling on Convolutional Neural Networks. Additionally, we propose a parallel cross-validation method that can speed up the validation process in a parallel environment.

The Market for Fake Reviews

We study the market for fake product reviews on Amazon.com. These reviews are purchased in large private internet groups on Facebook and other sites. We hand collect data on these markets to understand what products buy fake reviews and then collect a panel of data on these products' ratings and reviews on Amazon, as well as their sales rank, advertising, and pricing policies. We find that a wide array of products purchase fake reviews, including products with many reviews and high average ratings. Buying fake reviews on Facebook is associated with a significant but short-term increase in average rating and number of reviews. We exploit a sharp but temporary policy shift by Amazon to show that rating manipulation has a large causal effect on sales. Finally, we examine whether rating manipulation harms consumers or whether it is mostly used by high-quality or young products in a manner akin to advertising. We find that after firms stop buying fake reviews, their average ratings fall and the share of one-star reviews increases significantly, particularly for young products, indicating rating manipulation is mostly used by low-quality products and is deceiving and harming consumers.

Interface for Fake Product Review Detection, Analysis and Removal

Interface for Fake Product Review Detection, Analysis and Removal

Fake Product Review Monitoring & Removal and Sentiment Analysis of Genuine Reviews

Any E-Commerce website gets bad reputation if they sell a product which has bad review, the user blames the e-Commerce website rather than manufacturers most of the times. In some review sites some great audits are included by the item organization individuals itself so as to make so as to deliver false positive item reviews. To eliminate these type of fake product review, we will create a system that finds out the fake reviews and eliminates all the fake reviews by using machine learning. We also remove the reviews that are flood by a marketing agency in order to boost up the ratings of a particular product. Finally Sentiment analysis is done for the genuine reviews to classify them into positive and negative. We will use Bag-of-words to label individual words according to their sentiment.