Fake Reviews Research Articles

Impact of praise cashback strategy: Implications for consumers and e‐businesses

This study focuses on a broadly adopted but largely underexplored cashback strategy, the praise cashback strategy (PCS), which aims to entice consumers to keep purchases and post positive online reviews. The literature acknowledges the impact of cashback strategies on consumers’ purchase and review behaviors; however, it does not focus on how they affect consumers’ return behaviors. Moreover, there are few studies on the effect of cashback strategies on consumer surplus and social welfare in the presence of strategic consumers, especially in relation to analyzing the proportion of fake reviews. By incorporating consumers’ review and return behaviors into a newsvendor model, we establish a sequential procedure for merchants to determine pricing, inventory, and PCS decisions, and then examine the conditions under which merchants prefer to adopt a PCS and its impact. Our results reveal that the adoption of a PCS is a typical prisoner's dilemma for merchants in markets where PCSs are prevalent. Only when the PCS phenomenon is pervasive in the market, will merchants adopt a PCS with a small cashback amount. A PCS market negatively impacts consumer surplus and social welfare; however, merchants and consumers may benefit from a small cashback amount. Contrary to the common belief that a PCS undermines the organic evolution of online reviews and reduces return rates, we show that a PCS only elicits a few fake reviews, while merchants may face higher return rates from adopting it. These results benefit future studies on cashback strategies and fake reviews and provide evidence that supports the scientific governance of PCSs.

Recognition of Phony Reviews in E-commerce Platform

Abstract: The business and commerce of today are greatly influenced by online reviews. The majority of how consumers choose which online products to buy is based on user reviews. As a result, opportunistic people or organizations try to slant product reviews in order to serve their own agendas. Now days, fake online reviews are more common, people simply post phony reviews to market their products, to cheat customers etc. In E-commerce application fake reviews are more common and we need a system to predict these fake reviews. Proposed system uses machine learning algorithm to identify the fake reviews in an Ecommerce platform. We build a new E-commerce application and customer registers themselves, browse and purchase the products. For the purchased products customers post reviews, and our system will predict the fake reviews using an algorithm.

A Signaling Theory of the Online Consumer Review Policy

Abstract In this paper, we consider a two-period model of an experience good with a seller (informed player) and a consumer (uninformed player) in each period. In the model, we examine the seller’s decision about offering refunds for online reviews of his products, and each period consumer’s purchasing decision together with the first period consumer’s reviewing decision. Our main interest is whether a high-quality product seller offers a high amount or a low amount of cashback for an online review. We show that a lenient cashback policy for a review can be a signal of high quality of the product. Intuitively, a high-quality seller can offer a higher amount of cashback to a consumer who reviews, whether the review is positive or negative. This separation is possible mainly due to a difference in the second-period profits across types. We also briefly discuss the effect of the conditional review policy fostering fake reviews.

Fighting fake reviews: Authenticated anonymous reviews using identity verification

Fake reviews have become a pervasive problem in the realm of online commerce, affecting businesses and consumers alike. These fraudulent reviews can cause significant damage to the credibility of companies and negatively impact consumer welfare. While various platforms, such as Yelp and Amazon, have implemented measures to combat fake reviews, these efforts have been largely ineffective and, at times, even exacerbated the problem. As a result, on November 8, 2022, the Federal Trade Commission announced that it is soliciting input for possible regulations around ways to fight fake reviews. The growing sophistication of Artificial Intelligence—particularly generative AI technologies like ChatGPT—worsens the problem by enabling the production of human-like fake reviews at an unprecedented scale. This lends new urgency to the fake review problem, so it is imperative to examine the pros and cons of extant approaches and propose alternative approaches that are better equipped to tackle the issue. In this article, we introduce a novel approach using digital identity verification, which involves verifying a user’s identity via various forms of digital information that represent the individual and have not been applied to online reviews. We highlight the limitations of extant techniques and outline ways in which digital identity verification may be a promising solution to the problem of fake reviews. Potential benefits and challenges, as well as the effectiveness of our proposed approach in addressing the issue of fake reviews, are discussed.

Spam review detection with Metapath-aggregated graph convolution network

The large flux of online products in today’s world makes business reviews a valuable source for consumers for making sound decisions before making online purchases. Reviews are useful for readers in learning more about the product and gauge its quality. Fake reviews and reviewers form the bulk of the review corpus, making review spamming an open research challenge. These spam reviews require detection to nullify their contribution to product recommendations. In the past, researchers and communities have taken spam detection problems as a matter of serious concern. Yet, for all that, there is space for the performance of exploration on large-scale complex datasets. The work contributes towards robust feature selection with derived features that provide more details on malicious reviews and spammers. Ensemble and other standard machine learning techniques are trained and evaluated over optimal feature sets. In addition, the Metapath-based Graph Convolution Network (M-GCN) framework is proposed, which is an implicit knowledge extraction method to automatically capture the complex semantic meaning of reviews from the heterogeneous network. It makes analysis of triplet (users, reviews, and products) relationships in e-commerce sites through examination of Top-n feature sets in a mutually reinforcing manner. The proposed model is demonstrated on Yelp and Amazon benchmark datasets for evaluation of efficacy and it is shown outperforming state-of-the-art techniques with and without graph-utilization, providing an accuracy of 96% in the prediction task.

AN EMPIRICAL STUDY ON FAKE REVIEW DETECTION

In recent years, the Internet has opened up opportunities for manufacturers and retailers to advertise and sell their products online. Online shopping is becoming a habit of consumers. Although there are many benefits of buying and selling online, such as easy product selection and comparison from many different sellers before deciding which one to buy, reading comments before buying a product is a habit of customers. It helps them learn from the experiences of former buyers. However, buying based on product reviews is risky, especially fake reviews. These reviews affect the buyers’ purchase decisions. Detecting fake reviews is a critical problem. This study proposed a machine learning-based framework for detecting fake reviews by extracting features from text and deployed six machine-learning models for classification tasks. Experimental results showed that the SVC is a reliable machine-learning algorithm for classifying truthful reviews and fake reviews using the TF-IDF feature extraction technique.

Fake review detection in e-Commerce platforms using aspect-based sentiment analysis

Consumers rely on internet user reviews. Existing sentiment-based detection systems fail to capture consumer feelings regarding numerous aspects of products or services which influence their purchasing decisions. Despite the growing interest in detecting false reviews, prior studies have not explored the capacity to detect fake reviews for diverse products, which require distinct consumer experience. To overcome these problems, this paper proposes a fake review detection model using aspect-based sentiment analysis (ABSA) while considering the effects of product types. Using a dataset of Amazon reviews, our ABSA model revealed that two aspects are fundamental for detecting fake reviews and suggests the need to associate the two. These are the product category and the verified purchase attribute (with the greatest contribution observed for credence and experience product types).

Α ranking model based on user generated content and fuzzy logic

User Generated Comments and ranking based on it, has been studied extensively due to its impact on consumers and businesses. Literature highlights that platforms’ rankings might be deceiving due to fake reviews or rates that do not correspond to reality, but existing ranking models are a ‘black box’ thus, they cannot be assessed. This study fills in this gap by proposing a step-by-step ranking model which is based on comments rather than rates or the theoretically established criteria (e.g., food, service, atmosphere), which are not capable to portray the complexity of the dining experience. The proposed ranking model extracts topics from qualitative data (documents) and converts them into quantitative values through the lens of fuzzy logic. The topics are used as the ranking space while, pairwise comparisons between items (restaurants) are performed to extract a ranking ladder. We applied our model to TripAdvisor’s documents from restaurants in Athens, Greece.

Fake review detection system for online E-commerce platforms: A supervised general mixed probability approach

Online consumer reviews play an important role in helping consumers judge the quality and authenticity of products on e-commerce platforms. However, the constant presence of fake reviews on these platforms has significantly impacted the operation and development of e-commerce platforms. In this study, we develop a novel supervised probabilistic method to detect fake reviews by utilizing the difference in the distribution of non-fraudulent reviews and that of fake reviews. Specifically, we first derive the univariate distributions of several unique features (linguistic, behavioral, and interrelationship features). We then integrate these distributions into two mixed distributions according to their labels to represent the overall difference between non-fraudulent reviews and fake reviews. Next, we randomly generate synthetic review data points with different labels from the above mixed distributions. Finally, we train a Multilayer Perceptron model by using these synthetic review data to obtain a classifier. We conducted several experiments to test the model using several original real-world review datasets. Numerical results indicated that the proposed supervised method outperformed some well-known sampling models and fake review detection methods, in terms of classification accuracy. Moreover, we extend the proposed method to handle the scenarios with small samples of raw review data. This study contributes to the literature by exploiting the difference in the distribution of non-fraudulent reviews and that of fraudulent reviews, which can improve the accuracy of fake review detection for online platforms.

An explainable ensemble of multi-view deep learning model for fake review detection

Online reviews significantly impact consumers who are purchasing or seeking services via the Internet. Businesses and review platforms need to manage these online reviews to avoid misleading customers through fake ones. This necessitates developing intelligent solutions to detect these fake reviews and prevent their negative impact on businesses and customers. Therefore, many fake review detection models have been proposed to help distinguish fake reviews from genuine ones. However, these techniques depend on a limited perspective of features, mainly review content, to detect fake reviews, leading to poor performance in discovering the new patterns of fake review content and the dynamic behaviour of spammers. Therefore, there is still a need to develop new solutions to detect the new patterns of fake reviews. Hence, this paper proposes an explainable multi-view deep learning model to identify fake reviews based on different feature perspectives and classifiers. The proposed model can extract essential features from different perspectives, including review content, reviewer data, and product description. Moreover, we employ an ensemble approach that combines three popular deep learning algorithms: Bi-LSTM, CNN, and DNN, to enhance the performance of the fake review detection model. The results of two real-life datasets presented demonstrated the efficiency of our proposed model, where it outperformed the state-of-the-art methods with improvements ranging from 1% to 7% in terms of the AUC metric. To provide visibility into the outcomes of our proposed model and demonstrate the trust and transparency in the obtained results, we also offer a comprehensive explanation for our model results using Shapely Additive Explanations (SHAP) method and attention techniques. The experimental results prove that our proposed model can provide reasonable explanations that help users understand why specific reviews are classified as fake.

Development of a method for processing, grouping and annotating materials from reference and information sites in order to identify fake reviews using the naive Bayes classifier algorithm

Development of a method for processing, grouping and annotating materials from reference and information sites in order to identify fake reviews using the naive Bayes classifier algorithm

From Fake Reviews to Fake News: A Novel Pandemic Model of Misinformation in Digital Networks

Digital networks and E-commerce platforms have had a profound effect on people’s personal, educational, and professional life all around the world. They offer space for advertising, sales, and disseminating news and information, even if they are frequently used for social marketing, interacting, and sharing thoughts among people. Currently, most E-commerce platforms utilize digital network space for advertisement and an increasing trend of social commerce is visible in all parts of the world. During the Post-COVID-19 pandemic, a rapid increase in digital media and E-commerce usage was observed in all parts of the world for personal and professional aspects. The increase in misinformation through these platforms is a major challenge that the current governments face today as rumors and fake news creates severe detrimental implications in society. In this work, we consider fake reviews and misinformation in online digital networks as a single disease, and thereby, by considering the recent trends in online social media marketing, we formulate a pandemic model for digital networks with a psychological state of human choice. The positivity and stability of the model are mathematically tested and validated. Our analysis and simulation prove that the system is stable and justifiable in the real-world digital environment. The generated pandemic model can be applied to assess the social and emotional intelligence of communities and consumers who are frequently exposed to misinformation and share fake news.

Unmasking deception: a CNN and adaptive PSO approach to detecting fake online reviews.

Online reviews play a critical role in modern word-of-mouth communication, influencing consumers' shopping preferences and purchase decisions, and directly affecting a company's reputation and profitability. However, the credibility and authenticity of these reviews are often questioned due to the prevalence of fake online reviews that can mislead customers and harm e-commerce's credibility. These fake reviews are often difficult to identify and can lead to erroneous conclusions in user feedback analysis. This paper proposes a new approach to detect fake online reviews by combining convolutional neural network (CNN) and adaptive particle swarm optimization with natural language processing techniques. The approach uses datasets from popular online review platforms like Ott, Amazon, Yelp, TripAdvisor, and IMDb and applies feature selection techniques to select the most informative features. The paper suggests using attention mechanisms like bidirectional encoder representations from transformers and generative pre-trained transformer, as well as other techniques like Deep contextualized word representation, word2vec, GloVe, and fast Text, for feature extraction from online review datasets. The proposed method uses a multimodal approach based on a CNN architecture that combines text data to achieve a high accuracy rate of 99.4%. This outperforms traditional machine learning classifiers in terms of accuracy, recall, and F measure. The proposed approach has practical implications for consumers, manufacturers, and sellers in making informed product choices and decision-making processes, helping maintain the credibility of online consumer reviews. The proposed model shows excellent generalization abilities and outperforms conventional discrete and existing neural network benchmark models across multiple datasets. Moreover, it reduces the time complexity for both training and testing.

A Comparative Study of Sentiment Analysis Methods for Detecting Fake Reviews in E-Commerce

The popularity of the e-commerce system has increased, especially under the COVID scenario. Consumer product reviews from the past have had a significant impact on influencing consumers' purchasing decisions. Fake reviews—those written by humans and computers that engage in dishonest behavior—are consequently generated to increase product sales. The fake reviews hurt consumers and are dishonest. The goal of this research is to examine and evaluate the performance of various methods for identifying fake reviews. The well-known and widely-used Amazon Review Data (2018) dataset was used for this research. The first 10 product categories on Amazon.com with favorable feedback will be provided in the data section. After that, perform fundamental data preparation procedures such as special character trimming, bag of words, TF-IDF, etc. The models are trained to create a dataset for detecting fake reviews. This research compares the performance of four different models: GPT-2, NBSVM, BiLSTM, and RoBERTa. The hyperparameters of the models are also tuned to find the optimal values. The research concludes that the RoBERTa model performs the best overall, with an accuracy of 97%. GPT-2 has an overall accuracy of 82%, NBSVM has an overall accuracy of 95%, and BiLSTM has an overall accuracy of 92%. The research also calculates the Area Under the Curve (AUC) for each model and finds that RoBERTa has an AUC of 0.9976, NBSVM has an AUC of 0.9888, BiLSTM has an AUC of 0.9753, and GPT-2 has an AUC of 0.9226. It can be observed that the RoBERTa model has the highest AUC value, which is close to 1. Therefore, it can be concluded that this model provides the most accurate prediction for detecting fake reviews, which is the main focus of this research. Doi: 10.28991/HIJ-2023-04-02-08 Full Text: PDF

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.

Online Fake Review Detection Using SVM

Abstract: After the pandemic, our overall life is changing and challenging that’s why our demand is changing now, we are focused on wellness, sustainability, technology, and the gig economy because of these trends we can observe the reflection in the changing desired need and limitation of seller and customer. Many customers can post a review on any website after making a purchase. Whether it’s an online purchase or an offline retail purchase. When customers buy a product online, they check the product reviews. This is very important for today’s e-commerce product decisions. There is a financial gain associated with writing fake reviews, which is why there has been a significant increase in misleading statements about certain product reviews on websites. Misleading reviews are dangerous reviews. Positive product reviews can attract customers and increase sales. Negative product reviews can reduce demand for that product and reduce sales. These misleading reviews are dangerous to your product’s reputation. In this paper, we use machine learning algorithms which are SVM Support vector machines, which are one of the most popular supervised learning algorithms used for both classification and regression problems. However, it is mainly used for machine-learning classification problems

Product Fake Reviews Detection with Sentiment Analysis Using Machine Learning

Abstract: Recently, Sentiment Analysis (SA) has become one of the most interesting topics in text analysis, due to its promising commercial benefits. One of the main issues facing SA is how to extract emotions inside the opinion, and how to detect fake positive reviews and fake negative reviews from opinion reviews. Moreover, the opinion reviews obtained from users can be classified into positive or negative reviews, which can be used by a consumer to select a product. This paper aims to classify product reviews into groups of positive or negative polarity by using machine learning algorithms. In this study, we analyse online product reviews using SA methods in order to detect fake reviews. SA and text classification methods are applied to a dataset of product reviews. More specifically, we compare five supervised machine learning algorithms: Support Vector Machine (SVM), for sentiment classification of reviews using two different datasets, including product review dataset V2.0 and product reviews dataset V1.0. The measured results of our experiments show that the SVM algorithm outperforms other algorithms, and that it reaches thehighest accuracy not only in text classification, but also in detecting fake reviews.

DHMFRD – TER: a deep hybrid model for fake review detection incorporating review texts, emotions, and ratings

DHMFRD – TER: a deep hybrid model for fake review detection incorporating review texts, emotions, and ratings

RETRACTED ARTICLE: Transparency in healthcare and e-commerce: detecting online fake reviews using a dense neural network model with relevance mapping

RETRACTED ARTICLE: Transparency in healthcare and e-commerce: detecting online fake reviews using a dense neural network model with relevance mapping

Exploring Latent Characteristics of Fake Reviews and Their Intermediary Role in Persuading Buying Decisions

Online reviews play a significant role in shaping consumer purchase decisions. Accordingly, emergence of fake reviews has proliferated as an instrument to manipulate customers’ buying preferences. Such manifestation, however, lacks theoretical grounding and remains under researched due to two notable challenges: first, absence of conceptual underpinnings between consumers’ writing style and recommendation behavior. Second, little knowledge about the role of product characteristics underlying fake reviews and their influence on nudging product preferences. Through the lens of environmental psychology, this study uses an empirical investigation utilizing natural language processing (NLP) to uncover latent product-specific features underlying customer reviews and their impact on persuading buying preferences. As a major finding, we observe that characteristics underlying fake reviews, as opposed to genuine ones, fail to influence product recommendation or discouragement. Accordingly, we suggest firms permitting fake reviews on their portals to be aware of the limited economic advantages of such practices.