Machine learning-based Fake reviews detection with amalgamated features extraction method

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.

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.

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.

Online product reviews have become increasingly important in digital consumer markets where they play a crucial role in making purchasing decisions by most consumers. Unfortunately, spammers often take advantage of online reviews by writing fake reviews to promote/demote certain products. Most of the previous studies have focused on detecting fake reviews and individual fake reviewer-ids. However, to target a particular product, fake reviewers work collaboratively in groups and/or create multiple fake ids to write reviews and control the sentiments of the product. This article addresses the problem of finding such fake reviewer groups. More specifically, we propose a top-down framework for candidate fake reviewer groups' detection based on the DeepWalk approach on reviewers' graph data and a (modified) semisupervised clustering method, which can incorporate partial background knowledge. We validate our proposed framework on a real review dataset from the Google Play Store, which has partial ground-truth information about 2207 fraud reviewer-ids out of all 38 123 reviewer-ids in the dataset. Our experimental results demonstrate that the proposed approach is able to identify the candidate spammer groups with reasonable accuracy. The proposed approach can also be extended to detect groups of opinion spammers in social media (e.g. fake comments or fake postings) with temporal affinity, semantic characteristics, and sentiment analysis.

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.

Gaming the system: Fake online reviews v. consumer law

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.

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.

With the rapid advancement of virtual human technology and live-streaming e-commerce, virtual anchors have increasingly become key interactive entities in the digital economy. However, emerging issues such as fake reviews, abnormal tipping, and illegal transactions pose significant threats to platform financial security and user privacy. To address these challenges, a multimodal emotion–finance fusion security recognition framework (MSF-Net) is proposed, which integrates visual, audio, textual, and financial transaction signals to achieve cross-modal feature alignment and multi-signal risk modeling. The framework consists of three core modules: the multimodal alignment transformer (MAT), the fake review detection (FRD) module, and the multi-signal fusion decision module (MSFDM), enabling deep integration of semantic consistency modeling and emotion–behavior collaborative recognition. Experimental results demonstrate that MSF-Net achieves superior performance in virtual live-streaming financial security detection, reaching a precision of 0.932, a recall of 0.924, an F1-score of 0.928, an accuracy of 0.931, and an area under curve (AUC) of 0.956, while maintaining a real-time inference speed of 60.7 FPS, indicating outstanding precision and responsiveness. The ablation experiments further verify the necessity of each module, as the removal of any component leads to an F1-score decrease exceeding 4%, confirming the structural validity of the model’s hierarchical fusion design. In addition, a lightweight version of MSF-Net was developed through parameter distillation and quantization pruning techniques, achieving real-time deployment on mobile devices with an average latency of only 19.4 milliseconds while maintaining an F1-score of 0.923 and an AUC of 0.947. The results indicate that MSF-Net exhibits both innovation and practicality in multimodal deep fusion and security risk recognition, offering a scalable solution for intelligent risk control in data-driven artificial intelligence applications across financial and virtual interaction domains.

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.