Detecting Fake Reviews Using Aspect-Based Sentiment Analysis and Graph Convolutional Networks

Online reviews significantly influence consumer purchase decisions and business reputations on digital platforms. However, the widespread presence of fake reviews not only misguides consumers but also undermines the reliability of online reviews. Therefore, detecting fake reviews is crucial for various stakeholders and has evolved from simple visual inspection to the use of advanced machine learning models. Despite the proven effectiveness of machine learning models in fake review detection, several key research gaps remain.First, most predictive models rely on binary classification (i.e., true vs. fake), which assumes that review authenticity is a clear-cut distinction between real and fake. However, online reviews often lie on a spectrum of deception, where subtle manipulation, exaggeration, and subjective bias blur the boundary between authenticity and fabrication. Second, prior research lacks a clearly structured framework that accounts for behavioural features in predicting deceptive reviews.To address these gaps, this study develops a probabilistic modelling framework that: 1) estimates the likelihood of deception on a continuous spectrum, allowing for more flexible and accurate detection beyond a rigid true/false label; 2) systematically organizes behavioural features into three dimensions – structural (e.g., number of friends), relational (e.g., number of compliments) and activity-based (e.g., review frequency), based on Social Capital Theory; 3) incorporates store-level attributes, such as average rating, number of reviews, and consumer-generated “vibes”.This study uses a dataset of 32,964 restaurant reviews from Yelp, covering both real and fake reviews from January 2005 to November 2024. A range of probabilistic machine models – including Logistic Regression, Decision Tree, Random Forest, XGBoost, and Artificial Neural Networks – are implemented and compared using 5-fold cross-validation. XGBoost is selected as the final model based on overall superior performance across Precision, Recall, F1-Score, Accuracy, and Area Under the ROC Curve (AUC).Results show that relational and structural behavioural features are among the top predictors. Interestingly, reviewers high in social capital features – such as elite reviewers (designated by Yelp as “elite” based on their activity, consistency, and perceived credibility) – are more likely to generate fake reviews. The average probability of fake review among elite reviewers is 33.5%, compared to 12% among non-elite reviewers. At the store level, reviews associated with high-rated businesses tend to exhibit higher probabilities of being fake. Furthermore, the distribution of fakeness probability across star ratings follows a U-shaped pattern, with reviews at 1-star and 5-star exhibiting higher probabilities of deception, and a secondary peak observed at 4-star.This study contributes to the literature by developing a systematic and behaviourally grounded probabilistic model to predict fake online reviews. Practically, detecting the probabilities of fake review enhances review transparency for consumers, protects brand reputation for businesses, and support fair review policies for regulators.

Impacts of consumer cognitive process to ascertain online fake review: A cognitive dissonance theory approach

Online reviews are a growing market, but it is struggling with fake reviews. They undermine both the value of reviews to the user, and their trust in the review sites. However, fake positive reviews can boost a business, and so a small industry producing fake reviews has developed. The two sides are facing an arms race that involves more and more natural language processing (NLP). So far, NLP has been used mostly for detection, and works well on human-generated reviews. But what happens if NLP techniques are used to generate fake reviews as well? We investigate the question in an adversarial setup, by assessing the detectability of different fake-review generation strategies. We use generative models to produce reviews based on meta-information, and evaluate their effectiveness against deceptiondetection models and human judges. We find that meta-information helps detection, but that NLP-generated reviews conditioned on such information are also much harder to detect than conventional ones.

Online reviews play an integral part for success or failure of businesses. Prior to purchasing services or goods, customers first review the online comments submitted by previous customers. However, it is possible to superficially boost or hinder some businesses through posting counterfeit and fake reviews. This paper explores a natural language processing approach to identify fake reviews. We present a detailed analysis of linguistic features for distinguishing fake and trustworthy online reviews. We study 15 linguistic features and measure their significance and importance towards the classification schemes employed in this study. Our results indicate that fake reviews tend to include more redundant terms and pauses, and generally contain longer sentences. The application of several machine learning classification algorithms revealed that we were able to discriminate fake from real reviews with high accuracy using these linguistic features.

With the widespread of fake online reviews, the detection of fake reviews has become a hot research issue. Despite the efforts of existing studies on fake review detection, the issues of imbalanced data and feature pruning still lack sufficient attention. To address these gaps, the present study proposes an ensemble model for the detection of fake online reviews. The model consists of four steps, and the first three steps are proposed to optimize the base classifiers: (i) Data resampling: We propose a novel way to address the data imbalance problem by combining the resampling and the grid search technique. (ii) Feature pruning: We propose an ablation study to drop unimportant features. (iii) Parameters optimization: We apply the grid search algorithm to determine suitable values of the relevant parameters for each base classifier. (iv) Classifier ensembling: We apply majority voting and stacking strategies to integrate the optimized base classifiers. The proposed data resampling method is also applied for the meta-classifier in the stacking ensemble model. This study produces advances in terms of combining different methods or algorithms into a model and the results show that the proposed ensemble model outperforms some existing techniques, thereby providing a new way to solve the data imbalance and feature pruning issues in the field of fake review detection.

Given users and products that he/she reviews, can we recognize fake reviews just using the text information, or determine whether a reviewer is a fraud or not? Automatically detecting fake reviews and reviewers is an urgent problem and lots of work attempts for discovering linguistics, behaviors and graph patterns. However, in reality, there are new kinds of fraudsters who can change their behaviors to camouflage as genuine reviewers to avoid detection systems. With the fraudsters become distributed, dynamic, and adversarial, anti-spam tasks face a new challenge. In this paper, we tackle the challenge of adversarial fraudsters in online app review platform and propose a system called DDF (Detect, Defense, and Forecast) to uncover camouflage accounts. Firstly, we select a small set of seed with high-precision based on text and behavior features; Secondly, we build our graph-based detection model for uncovering hidden (distant) users who serve structurally similar to the seed by utilizing Graph Convolutional Network (GCN) algorithm. Thirdly, we evaluate DDF using real-world data set from Tencent APP Store and analyze the potential fraudsters detected by DDF. It is worth mentioning that precision can achieve 0.95+. Finally, we validate the efficiency and scalability of DDF and show that it can be well transferred to other anti-spam tasks.

Consumers have become ever more reliant on online reviews. Therefore, fake reviews have also become increasingly rampant and eroded online review platforms’ credibility. Previous literature suggests that particular linguistic styles can manifest in fake reviews with reference to the varying stages of the language-production process. Drawing on the language-production model as our theoretical foundation, we examine the psycholinguistic styles of fake online reviews at the message and formulation level. We performed a computational linguistic analysis on 66,940 reviews from Yelp. Our results suggest that fake reviews align more with deceptive writing in terms of the message-level variables such as length and psychological (affective, cognitive, social, and perceptual) cues. Interestingly, we found that they align less with deceptive writing in terms of the formulation-level variables such as readability, pronouns, and part-of-speech tags, which may be due to the fake review writers’ conscious attempt to follow the language styles that genuine reviews adopt.

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

Gaming the system: Fake online reviews v. consumer law

Online reviews significantly impact consumers' decision-making process and firms' economic outcomes and are widely seen as crucial to the success of online markets. Firms, therefore, have a strong incentive to manipulate ratings using fake reviews. This presents a problem that academic researchers have tried to solve for over two decades and on which platforms expend a large amount of resources. Nevertheless, the prevalence of fake reviews is arguably higher than ever. To combat this, we collect a dataset of reviews for thousands of Amazon products and develop a general and highly accurate method for detecting fake reviews. A unique difference between previous datasets and ours is that we directly observe which sellers buy fake reviews. Thus, while prior research has trained models using laboratory-generated reviews or proxies for fake reviews, we are able to train a model using actual fake reviews. We show that products that buy fake reviews are highly clustered in the product reviewer network. Therefore, features constructed from this network are highly predictive of which products buy fake reviews. We show that our network-based approach is also successful at detecting fake review buyers even without ground truth data, as unsupervised clustering methods can accurately identify fake review buyers by identifying clusters of products that are closely connected in the network. While text or metadata can be manipulated to evade detection, network-based features are more costly to manipulate because these features result directly from the inherent limitations of buying reviews from online review marketplaces, making our detection approach more robust to manipulation.

The increasing popularity of e-commerce and online review platforms has encouraged consumers to rely more heavily on reviews before purchasing skincare products. However, the proliferation of fake reviews presents a new challenge for consumers in making decisions. This study aims to detect fake reviews of skincare products by applying the Support Vector Machine (SVM) algorithm as the main model. Data was taken from the Female Daily website, which contains a collection of reviews of COSRX skincare products. This study applied text mining and data preprocessing techniques to prepare 8,880 unstructured reviews into a format ready for analysis. The SVM model was used to detect genuine and fake reviews based on linguistic patterns in the text. The test results showed excellent accuracy, with the model accurately predicting 5,900 fake reviews and 2,758 genuine reviews, with an error rate of only around 2.5% of the total data. These findings indicate that SVM is an effective method for detecting fake skincare product reviews and has the potential to be implemented as a decision support tool for consumers and online review platforms.

With the advancement of internet technology, customers increasingly rely on online reviews as a valuable source of information. The study aims to develop a marketing data analytics framework to manage online reviews, especially fake reviews, which have become a significant issue undermining the creditability of online review systems. As small and medium-sized enterprises often lack the capabilities to automatically derive customer insights from online reviews, this study proposes a cost-effective, extensible Review-Analytics-as-a-Service (RAaaS) framework that can be operated by non-data specialists to facilitate online review data analytics. We demonstrate the framework’s application by using two datasets with more than 400,000 online reviews from Yelp to simulate live platforms and demonstrate an analytic flow of review fraud detection and understanding. The findings reveal insights into the influence of fake reviews on product ranking and exposure rate. Moreover, it was found that there was a higher concentration of sadness and anger in fake reviews (vs. organic reviews). In addition, fake reviews tend to be shorter, more extreme (with the use of strong adverbs), and have different patterns of topic distribution. This study has important implications for different stakeholder groups including, but not limited to, SMEs, review platforms and customers.

Online product reviews play a major role in the success or failure of an E-commerce business. Before procuring products or services, the shoppers usually go through the online reviews posted by previous customers to get recommendations of the details of products and make purchasing decisions. Nevertheless, it is possible to enhance or hamper specific E-business products by posting fake reviews, which can be written by persons called fraudsters. These reviews can cause financial loss to E-commerce businesses and misguide consumers to take the wrong decision to search for alternative products. Thus, developing a fake review detection system is ultimately required for E-commerce business. The proposed methodology has used four standard fake review datasets of multidomains include hotels, restaurants, Yelp, and Amazon. Further, preprocessing methods such as stopword removal, punctuation removal, and tokenization have performed as well as padding sequence method for making the input sequence has fixed length during training, validation, and testing the model. As this methodology uses different sizes of datasets, various input word-embedding matrices of n-gram features of the review's text are developed and created with help of word-embedding layer that is one component of the proposed model. Convolutional and max-pooling layers of the CNN technique are implemented for dimensionality reduction and feature extraction, respectively. Based on gate mechanisms, the LSTM layer is combined with the CNN technique for learning and handling the contextual information of n-gram features of the review's text. Finally, a sigmoid activation function as the last layer of the proposed model receives the input sequences from the previous layer and performs binary classification task of review text into fake or truthful. In this paper, the proposed CNN-LSTM model was evaluated in two types of experiments, in-domain and cross-domain experiments. For an in-domain experiment, the model is applied on each dataset individually, while in the case of a cross-domain experiment, all datasets are gathered and put into a single data frame and evaluated entirely. The testing results of the model in-domain experiment datasets were 77%, 85%, 86%, and 87% in the terms of accuracy for restaurant, hotel, Yelp, and Amazon datasets, respectively. Concerning the cross-domain experiment, the proposed model has attained 89% accuracy. Furthermore, comparative analysis of the results of in-domain experiments with existing approaches has been done based on accuracy metric and, it is observed that the proposed model outperformed the compared methods.

Online review plays a crucial role in many current e-commerce applications. However, fake reviews would mislead users. Therefore, detecting such reviews is an important task for safeguarding the interests of users. But the review sequence has been neglected by the former work. In this paper, we explore the issue on fake review detection in review sequence, which is crucial for implementing online anti-opinion spam. We first analyze the characteristics of fake reviews. Based on review contents and reviewer behaviors, six time sensitive features are proposed to find the fake reviews. And then, we devise two type of detection methods, the supervised and the threshold-based, for spotting the fake reviews as early as possible. Finally, we carry out intensive experiments on a real-world review set to verify the effectiveness of our methods. The experimental results show that our methods can identify the fake reviews orderly with high precision and recall.

Consumers reviews on ecommerce websites, online services, ratings and experience stories are useful for the user as well as the vendor. The reviewer can increase their brand’s loyalty and help other customers understand their experience with the product. Similarly reviews help the vendors gain more profiles by increasing their sale of products, if consumers leave positive feedback on their product review. But unfortunately, these review mechanisms can be misused by vendors. For example, one may create fake positive reviews to promote brand’s reputation or try to demote competitor’s products by leaving fake negative reviews on their product. Existing solutions with supervised include application of different machine learning algorithms and different tools like Weka. Unlike the existing work, instead of using a constrained dataset I chose to have a wide variety of vocabulary to work on such as different subjects of datasets combined as one big data set. Sentiment analysis has been incorporated based on emojis and text content in the reviews. Fake reviews are detected and categorized. The testing results are obtained through the application of Naïve Bayes, Linear SVC, Support Vector Machine and Random forest algorithms. The implemented (proposed) solution is to classify these reviews into fake or genuine. The highest accuracy is obtained by using Naïve Bayes by including sentiment classifier.