An Ensemble Model for Fake Online Review Detection Based on Data Resampling, Feature Pruning, and Parameter Optimization

Gaming the system: Fake online reviews v. consumer law

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.

Product fake reviews are increasing as the trend is changing toward online sales and purchases. Fake review detection is critical and challenging for both researchers and online retailers. As new techniques are introduced to catch the fake reviewer, so are their intruding approaches. In this paper, different features are amalgamated along with sentiment score to design a model that checks the model performance under different classifiers. For this purpose, six supervised learning algorithms are utilized to build the fake review detection models, utilizing LIWC, unigrams, and sentiment score features. Results show that the amalgamation of selected features is a better approach to fake review detection, achieving an accuracy score of 88.76%, which is promising compared to similar other work.

Nowadays, O2O commercial platforms are playing a crucial role in our daily purchases. However, some people are trying to manipulate the online market maliciously by opinion spamming, a kind of web fraud behavior like writing fake reviews, due to fame and profits, which will harm online purchasing environment and should be detected and eliminated. Moreover, manual fake reviewers are more deceptive compared with old web spambots. Although several efficient methods were proposed in the fake review detection field, the manual fake reviewers are also evolving rapidly. They imitate to be benign users to control the velocity of review fraud actions, and deceive the detection system. Our investigation presented that geolocation factor is potential and can well reflect the distinctions between fake reviewers and benign users. In this research, we analyzed the geolocations of shops in reviews, found the distinct distribution features of those in fake reviewers and benign users, and proposed a SpamTracer model that can identify fake reviewers and benign users by exploiting an improved HMM (Hidden Markov Model). Our experiment demonstrated that SpamTracer could achieve 71% accuracy and 76% recall in the unbalanced dataset, outperforming some excellent classical approaches in the aspect of stability. Furthermore, SpamTracer can help to analyze the regularities of review fraud actions. Those regularities reflect the time and location in which online shops are likely to hire fake reviewers to increase their turnover. We also found that a small group of fake reviewers tend to work with plural shops located in a small business zone.

In e-commerce, user reviews can play a significant role in determining the revenue of an organisation. Online users rely on reviews before making decisions about any product and service. As such, the credibility of online reviews is crucial for businesses and can directly affect companies’ reputation and profitability. That is why some businesses are paying spammers to post fake reviews. These fake reviews exploit consumer purchasing decisions. Consequently, the techniques for detecting fake reviews have extensively been explored in the past twelve years. However, there still lacks a survey that can analyse and summarise the existing approaches. To bridge up the issue, this survey paper details the task of fake review detection, summing up the existing datasets and their collection methods. It analyses the existing feature extraction techniques. It also summarises and analyses the existing techniques critically to identify gaps based on two groups: traditional statistical machine learning and deep learning methods. Further, we conduct a benchmark study to investigate the performance of different neural network models and transformers that have not been used for fake review detection yet. The experimental results on two benchmark datasets show that RoBERTa performs about 7% better than the state-of-the-art methods in a mixed domain for the deception dataset with the highest accuracy of 91.2%, which can be used as a baseline for future studies. Finally, we highlight the current gaps in this research area and the possible future directions.

Ontology based sentiment analysis for fake review detection

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.

Fake online reviews hinder internet marketing efforts to build businesses and brands in a competitive market with changing consumer expectations. This helps brands attract clients, making fake online reviews hard to uncover. Hence, fake reviews and websites are extensively examined. AI models like the Generalized Additive2 Model (GA2M) and its ensemble with the Elastic-net Classifier model have been studied using Log-Loss metric. This research, analysis, and depiction help demarcate bogus hotel reviews and websites from genuine entities. The paper uses ML classifiers (Decision Tree, Logistic Regression, Naïve Bayes) and ensemble models (Random Forest, Gradient Boosting) to identify legitimate websites using binary classification. This article compares ML classifiers and ensemble models by accuracy, precision, recall, f1-score, and ROC-AUC to evaluate their pros and downsides. Elastic-Net Classifier (L2 / Binomial Deviance) with score of 0.2879 outperformed GA2M model by 0.66% in LogLoss holdout score on Hotel dataset. LogLoss predicts values better than ROC-AUC due to its closer proximity to predicting actual values. Elastic-Net Classifier (L2 / Binomial Deviance) surpassed GA2M in F1 score, precision, and accuracy by 0.4%, 1.84%, and 0.63%. Ensemble techniques outperform ML classifiers in the Fraudulent and Legitimate Online Shops dataset with ROC-AUC scores of 0.71%, 1.73%, 0.76%, 1.10%, and 0.63% using 50% to 90% training datasets and 50% to 10% holdout datasets.

Detecting review spam is important for current e-commerce applications. However, the posted order of review 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 analyze the characteristics of fake reviews firstly. Based on review contents and reviewer behaviors, six time sensitive features are proposed to highlight the fake reviews. And then, we devise supervised solutions and a threshold-based solution to spot the fake reviews as early as possible. The experimental results show that our methods can identify the fake reviews orderly with high precision and recall.

Fake review detection and its elimination from the given dataset using different Natural Language Processing (NLP) techniques is important in several aspects. In this article, the fake review dataset is trained by applying two different Machine Learning (ML) models to predict the accuracy of how genuine are the reviews in a given dataset. The rate of fake reviews in E-commerce industry and even other platforms is increasing when depend on product reviews for the item found online on different websites and applications. The products of the company were trusted before making a purchase. So this fake review problem must be addressed so that these large E-commerce industries such as Flipkart, Amazon, etc. can rectify this issue so that the fake reviewers and spammers are eliminated to prevent users from losing trust on online shopping platforms. This model can be used by websites and applications with few thousands of users where it can predict the authenticity of the review based on which the website owners can take necessary action towards them. This model is developed using Naïve Bayes and random forest methods. By applying these models one can know the number of spam reviews on a website or application instantly. To counter such spammers, a sophisticated model is required in which a need to be trained on millions of reviews. In this work “amazon Yelp dataset” is used to train the models and its very small dataset is used for training on a very small scale and can be scaled to get high accuracy and flexibility.

Online product reviews are an important driver of customers’ purchasing behavior. Fake reviews seriously mislead consumers, challenging the fairness of the online shopping environment. Although the detection of fake reviews has progressed, several problems remain. First, fake comment recognition ignores the correlation between time and the semantics of the comment texts, which is always hidden in the context of the reviews. Second, the impact of multi-source information on fake comment recognition is not considered, as it constitutes a complex, high-dimensional, heterogeneous relationship between reviewers, reviews, stores and commodities. To overcome these problems, the present paper proposes a dynamic knowledge graph-based method for fake-review detection. Based on the characteristics of online product reviews, it first extracts four types of entities using a developed neural network model called sentence vector/twin-word embedding conditioned bidirectional long short-term memory. Time series related features are then added to the knowledge graph construction process, forming dynamic graph networks. To enhance the fake-review detection, four indicators are newly defined for determining the relationships among the four types of nodes. In experimental evaluations, our method surpassed the state-of-the-art results.

This paper presents an approach to fake review detection, essentially for online hotel reviews, by combining the review-based approach and reviewer-based approach. Different Natural Language Processing techniques such as tokenization, lemmatization, vectorization, etc. are used to extract insightful features from the review text data. After text mining, the data is used to train different classification models using machine learning algorithms that detect fake reviews. After evaluating the models, a comparison is made based on the performance metrics. Furthermore, a web based user interface is created to provide a platform that combines the knowledge of the input user information with the chosen machine learning model to perform fake review detection on the input data.

Now a days the usage of Internet and online marketing has become very popular. Millions of products and services are available in online marketing that generate huge amount of information. Hence, it’s difficult to find the best suitable services or products compatible to the requirement. Customers directly take decision based on reviews or opinions that are written by others based on their experiences. In this competitive world any person can write anything, this raise the number of fake reviews. Various companies are hiring people to write fake positive reviews about their services or products or unfair negative reviews about their competitors’ services or products. This process gives wrong input to the new customers who wish to buy such items and hence we need a system to detect such fake reviews and remove them. In this paper we discuss various supervised, unsupervised and semi supervised data mining techniques for fake review detection based on different features.

Fake (deceptive) reviews have become a serious problem for online consumers, with the proliferation of online marketplaces leading to an increase in spurious reviews that are often used to lure or discourage potential customers. While sentiment analysis has been introduced to the e-commerce sector, the lack of an effective method to differentiate between authentic and fake reviews is still a major challenge. Existing approaches face issues such as slow convergence and inadequate precision. In order to address these challenges, this paper proposes a new approach that integrates sentiment features into the review detection process. The proposed approach uses a feature extraction method that utilizes a preconstructed sentiment dictionary, a pre-trained BERT model to extract feature vectors, and a fully connected dense layer to classify reviews as real or fake using the softMax function. The effectiveness of the proposed approach was evaluated on the Yelp dataset, showing a nearly 7% improvement in accuracy compared to existing feature sets and a nearly 4% improvement over existing state-of-the-art methods. The integration of sentiment features has shown promising results in detecting fake reviews, which is crucial for ensuring a fair and trustworthy online marketplace.

Information is passed by word-of-mouth figures prominently when consumers evaluate products through reviews. However, severe logistical problems are caused by the internet’s Water Army (i.e., literally people who are hired by individuals or organizations to compose false reviews), that flood the internet e-commerce websites. An array of internet e-commerce sites is flooded with inauthentic information, and false reviews are used to maliciously induce consumers to purchase specific products, that often contain some defects. Notwithstanding the fact that the internet Water Army first manifested in China, it can also exist in other countries. The rationale lies in the high profitability possible, in the minds of numerous organized underground paid poster groups, and in writing fake reviews to misinform consumers. It has become an increasingly daunting task to precisely spot the Water Army members, who often alter their writing style and posted content. In this paper, the authors devise a comprehensive set of features to characterize all users and compare the paid posters against the normal users on different dimensions; furthermore, an ensemble detection model equipped with seven disparate algorithms is put into place. Our model reached a score of 0.730 in the AUC measure, 0.691 in the F1 measure on the JD dataset, 0.926 in the AUC measure, and 0.871 in the F1 measure on the Amazon dataset, which outshines the measures in the existing research. The significance and contribution of this work are in advancing constructive solutions and recommendations for this major concern of the entire e-commerce industry.