Relevant bibliographies by topics / Fake review detection

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Fake review detection'

Author: Grafiati
Published: 20 February 2023
Last updated: 16 June 2025

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Journal articles on the topic "Fake review detection"

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S., Neha, and Anala A. "Fake Review Detection using Classification." International Journal of Computer Applications 180, no. 50 (2018): 16–21. http://dx.doi.org/10.5120/ijca2018917316.

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L, Jayalakshmi. "Fake Review Detection on Using Machine Learning on Online Product Selling Platform." International Journal for Research in Applied Science and Engineering Technology 10, no. 7 (2022): 3592–98. http://dx.doi.org/10.22214/ijraset.2022.45206.

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Abstract: The increasing popularity of online review systems motivates malevolent intent in competing sellers and service providers to manipulate consumers by fabricating product/service reviews. Immoral actors use Sybil accounts, Bot farms, and purchase authentic accounts to promote products and vilify competitors. Facing the continuous advancement of review spamming techniques, more research work is been carried out to assess the approaches explored to date to combat fake reviews, and regroup to define new ones. Fake reviews detection attracts many researchers’ attention due to the negative impacts on the society. Most existing fake reviews detection approaches mainly focus on semantic analysis of review’s contents. This project is aimed at fake review detection in online platform, to prevent damage due to deceptive reviews.We propose a Novel Fake Reviews Detection based on Logistic Regression technique
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Chettri, Ajanta, Amal George, Dr A. Rengarajan, and Feon Jaison. "Research Paper on Fake Online Reviews Detection using Semi-supervised and Supervised learning." International Journal for Research in Applied Science and Engineering Technology 10, no. 4 (2022): 1973–79. http://dx.doi.org/10.22214/ijraset.2022.41687.

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Abstract: Today's business and commerce are heavily influenced by online reviews. Most online product purchase decisions are based on customer reviews. As a result, opportunistic individuals or groups seek to shake product reviews in their favor. Fake online reviews have a significant impact on the efficiency of online consumers, merchants and e-commerce markets. Despite academic efforts to study fake reviews, there remains a need for research that can systematically analyze and summarize their causes and consequences. This task provides a semi-supervised and supervised text mining model for detecting fake web reviews and comparing their effectiveness to hotel review datasets. Keywords: Semi-Supervised. Supervised, Detection, Fake Review, Marketing
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Chettri, Ajanta, Amal George, Dr A. Rengarajan, and Feon Jaison. "Research Paper on Fake Online Reviews Detection using Semi-supervised and Supervised learning." International Journal for Research in Applied Science and Engineering Technology 10, no. 4 (2022): 1973–79. http://dx.doi.org/10.22214/ijraset.2022.41687.

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Abstract: Today's business and commerce are heavily influenced by online reviews. Most online product purchase decisions are based on customer reviews. As a result, opportunistic individuals or groups seek to shake product reviews in their favor. Fake online reviews have a significant impact on the efficiency of online consumers, merchants and e-commerce markets. Despite academic efforts to study fake reviews, there remains a need for research that can systematically analyze and summarize their causes and consequences. This task provides a semi-supervised and supervised text mining model for detecting fake web reviews and comparing their effectiveness to hotel review datasets. Keywords: Semi-Supervised. Supervised, Detection, Fake Review, Marketing
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Sun, Chengai, Qiaolin Du, and Gang Tian. "Exploiting Product Related Review Features for Fake Review Detection." Mathematical Problems in Engineering 2016 (2016): 1–7. http://dx.doi.org/10.1155/2016/4935792.

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Product reviews are now widely used by individuals for making their decisions. However, due to the purpose of profit, reviewers game the system by posting fake reviews for promoting or demoting the target products. In the past few years, fake review detection has attracted significant attention from both the industrial organizations and academic communities. However, the issue remains to be a challenging problem due to lacking of labelling materials for supervised learning and evaluation. Current works made many attempts to address this problem from the angles of reviewer and review. However, there has been little discussion about the product related review features which is the main focus of our method. This paper proposes a novel convolutional neural network model to integrate the product related review features through a product word composition model. To reduce overfitting and high variance, a bagging model is introduced to bag the neural network model with two efficient classifiers. Experiments on the real-life Amazon review dataset demonstrate the effectiveness of the proposed approach.
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Ram, Nikhil Chandra Sai, Gowtham Vakati, Jagadesh Varma Nadimpall, Yash Sah, and Sai Karthik Datla. "Fake Reviews Detection Using Supervised Machine Learning." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (2022): 3718–27. http://dx.doi.org/10.22214/ijraset.2022.43202.

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Abstract: With the continuous evolve of E-commerce systems, online reviews are mainly considered as a crucial factor for building and maintaining a good reputation. Moreover, they have an effective role in the decision making process for end users. Usually, a positive review for a target object attracts more customers and lead to high increase in sales. Nowadays, deceptive or fake reviews are deliberately written to build virtual reputation and attracting potential customers. Thus, identifying fake reviews is a vivid and ongoing research area. Identifying fake reviews depends not only on the key features of the reviews but also on the behaviours of the reviewers. This paper proposes a machine learning approach to identify fake reviews. In addition to the features extraction process of the reviews, this paper applies several features engineering to extract various behaviours of the reviewers. The paper compares the performance of several experiments done on a real Yelp dataset of restaurants reviews, we compare the performance of machine learning classifiers; KNN, Naive Bayes (NB), Logistic Regression. The results reveal that Logistic Regression outperforms the rest of classifiers in terms of accuracy achieving best. The results show that the system has better ability to detect a review as fake or original.
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Yang, Sheng Xiu, and Lu Jie Fan. "Survey on Review Spam of E-Commerce Sites." Applied Mechanics and Materials 631-632 (September 2014): 1190–93. http://dx.doi.org/10.4028/www.scientific.net/amm.631-632.1190.

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Online shopping reviews provide valuable information for customers to compare the quality of products, and many other aspects of future purchases. People increasingly rely on information from E-commerce reviews. Product reviews is an important determinant of potential customers’ buying choices. However, spammers are joining this community to try to mislead consumers by writing fake or unfair reviews to confuse the consumers. Fake product review detection makes an attempt to detect fake reviews and remove them to restore the truthful ones for readers. To the best of our knowledge, there is still less published study on this problem. In this paper, we make a survey and an attempt to give a brief overview on review spam. The related work of fake product review detection is presented including web spam and spam email. Then some methods to detect review spam are introduced and summarized. The trend of review spam detection is concluded finally.
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Lahire, Mayuri. "Survey on Comprehensive Study of Fake Reviews and Reviewers Detection using machine learning techniques." International Journal for Research in Applied Science and Engineering Technology 10, no. 3 (2022): 769–75. http://dx.doi.org/10.22214/ijraset.2022.40743.

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Abstract: Individuals and businesses are increasingly using opinionated social media, such as product evaluations, to make decisions. People, however, try to game the system for profit or fame by opinion spamming (e.g., creating bogus reviews) to promote or demote certain specific items. Such bogus reviews should be identified in order for reviews to reflect real user experiences and opinions. Most of the consumers are influenced by the online reviews on the product and it plays a crucial role in finalizing purchase decisions in the market. But fake reviewers or spammers misused and take advantage by writing fake reviews, positive fake reviews to promote the product, or negative fake reviews to demote the product. There has been huge research in this domain for more than a decade for detecting fake reviews or fake reviewers. Howsoever many fake reviewers work together by creating groups to target any product and writing fake reviews on product in bulk, reviewers create multiple fake IDs and write fake reviews. Detecting false reviews and specific fraudulent reviewers was the subject of previous work on opinion spam. The primary thing of this study is to give a strong and comprehensive relative study for detecting fake reviews and reviewers using machine learning. Keywords: Fake review, Fake reviewers Spam opinion, Opinion mining, FIM, Reviewer-centric spam, feature engineering
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Chen, Min, and Anusha Prabakaran. "Credibility Analysis for Online Product Reviews." International Journal of Multimedia Data Engineering and Management 9, no. 3 (2018): 37–54. http://dx.doi.org/10.4018/ijmdem.2018070103.

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With the prevalence of e-commerce, online product reviews are increasingly considered crowd-sourced consumer opinions that significantly influence customer purchasing decisions and product rankings. It is therefore important to ensure the truthfulness of reviews by detecting and filtering out fake/spam reviews. This article presents an effective framework to analyze review credibility for spam detection and opinion mining. It incorporates three methods: duplicated review detection, anomaly detection, and incentivized review detection, that complement each other to produce statistical credibility scores indicating review credibility. A practical end-to-end system is designed and developed accordingly, and is equipped with high-level data visualization for easy interpretation and summarization of the analysis results. Experiments on an Amazon review dataset demonstrate its efficiency, scalability and accuracy. This system could help e-commerce and consumers identify fake reviews, refine product rankings, and constrain vendors and spammers from engaging in dishonest practices.
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UG, Deekshitha K., and Deepa R. "Fake Product Review Monitoring System." International Journal for Research in Applied Science and Engineering Technology 10, no. 8 (2022): 1969–73. http://dx.doi.org/10.22214/ijraset.2022.46456.

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Abstract: In the current scenario, the data on the web is growing to a larger extent. Social Media is generating a large amount of data such as reviews, comments and customer’s opinions on a daily basis. This huge amount of user generated data is worthless unless some miningt e c h n i q u e s are applied to it. Nowadays, there are several people using social media reviews to order anything through online. Online spam detection is one of the herculean problems since there are many faux or fake reviews that are created by organizations or by the people themselvesfor various purposes. Such organizations tend to write fake reviews to mislead readersor automated detection systems by promoting or demoting the targeted productservices. Fake reviews detection has recentlybecome a limelight that’s capturing attention. Fake reviews are generated intentionally to mislead readers to believe false data that makes it tough and non-trivialto discover supported content. Hence, it is highly necessary to create a monitoring system which thoroughly checks for fake reviews among various product websites andremoves them promptly.
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Dissertations / Theses on the topic "Fake review detection"

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Ferreira, Uchoa Marina. "Detecting Fake Reviews with Machine Learning." Thesis, Högskolan Dalarna, Mikrodataanalys, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:du-28133.

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Many individuals and businesses make decisions based on freely and easily accessible online reviews. This provides incentives for the dissemination of fake reviews, which aim to deceive the reader into having undeserved positive or negative opinions about an establishment or service. With that in mind, this work proposes machine learning applications to detect fake online reviews from hotel, restaurant and doctor domains. In order to _lter these deceptive reviews, Neural Networks and Support Vector Ma- chines are used. Both algorithms' parameters are optimized during training. Parameters that result in the highest accuracy for each data and feature set combination are selected for testing. As input features for both machine learning applications, unigrams, bigrams and the combination of both are used. The advantage of the proposed approach is that the models are simple yet yield results comparable with those found in the literature using more complex models. The highest accuracy achieved was with Support Vector Machine using the Laplacian kernel which obtained an accuracy of 82.92% for hotel, 80.83% for restaurant and 73.33% for doctor reviews.
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Mohawesh, RIM. "Machine learning approaches for fake online reviews detection." Thesis, 2022. https://eprints.utas.edu.au/47578/.

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Online reviews have a substantial impact on decision making in various areas of society, predominantly in the arena of buying and selling of goods. The truthfulness of online reviews is critical for both consumers and vendors. Genuine reviews can lead to satisfied customers and success for quality businesses, whereas fake reviews can mislead innocent clients, influence customers’ choices owing to false descriptions and inaccurate sales. Therefore, there is a need for efficient fake review detection models and tools that can help distinguish between fraudulent and legitimate reviews to protect the ecosystem of e-commerce sites, consumers, and companies from these misleading fake reviews. Although several fake review detection models have been proposed in the literature, there are still some challenges regarding the performance of these models that need to be addressed. For instance, existing studies are highly dependent on linguistic features, but these are not enough to capture the semantic meaning of the reviews, necessary for improving prediction performance. Furthermore, when analysing a fake review text data stream, concept drift may occur where the input and output relationship of text changes over time, affecting model performance. The concept drift problem and its performance impact on fake review detection models has yet to be addressed. Moreover, existing models have only focused upon the review text, reviewer-based features, or both. They have also used a limited number of behavioural and content features that reduce the accuracy of the detection model. Further, these models have used fine-grained features (e.g., words) or coarse-grained features (e.g., documents, sentences, topics) separately, reducing the detection models’ performance. To address the above discussed research gaps, this research investigates and analyses the performance of different neural network models and advanced pre-trained models for fake review detection. Then, we propose an ensemble model that combines three transformer models to detect fake reviews on semi-real-world datasets. This research also investigates the concept drift problem by detecting the occurrence of concept drift within text data streams of fake reviews, finding a correlation between concept drift (if it exists) and the performance of detection models over time in the real-world data stream. Furthermore, this research proposes a new Interpretable ensemble of multi-view deep learning model (EEMVDLM) that can detect fake reviews based on different feature perspectives and classifiers and provide interpretability of deep learning models. This ensemble model comprises three popular machine learning models: bidirectional long-short-term-memory (Bi-LSTM), convolutional neural network (CNN), and deep neural network (DNN). Additionally, this model provides interpretations to achieve reliable results from deep learning models, which are usually considered as "Black Boxes". For this purpose, the shapley additive explanations (SHAP) method and attention mechanism are used to understand the underlying logic of a model and provide other hints to determine whether it is "unfair". In summary, this research provides the following contributions: (1) Comprehensive survey that analyses the task of fake review detection by providing the existing approaches, existing feature extraction techniques, challenges, and available datasets. (2) This research investigates and analyses the performance of different neural network models and transformers to demonstrate their effect on fake review detection. Experimental results show that the transformer models perform well with a small dataset for fake review detection. Specifically, the robustly optimised BERT pretraining approach (RoBERTa) achieves the highest accuracy. (3) This research proposes an ensemble of three transformer models to discover the hidden patterns in a sequence of fake reviews and detect them precisely. Experimental results on two semi-real datasets show that the proposed model outperforms the state-of-the-art methods. (4) This research provides an in-depth analysis for detecting concept drift within fake review data streams by using two methods: benchmarking concept drift detection methods and contentbased classification methods. The results demonstrate that there is a strong negative correlation between concept drift and the performance of fake review detection/prediction models, which indicates the difficulty of building more efficient models. (5) This research proposes a new Interpretable ensemble of multi-view deep learning model (EEMVDLM) that can detect fake reviews based on different feature perspectives and classifiers. The experimental results on two real-life datasets present excellent performance and outperformed the state-of-the-art methods. Further, the experimental results prove that our proposed model can provide reasonable interpretations that help users understand why certain reviews are classified as fake or genuine. To the best of our knowledge, this research provides the first study that incorporates advanced pre-trained models, investigates the concept drift problem, and the first Interpretable fake review detection approach. The findings of this thesis contribute to both practical and theoretical applications.
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Yap, Moi Hoon, Hassan Ugail, and R. Zwiggelaar. "Facial Analysis for Real-Time Application: A Review in Visual Cues Detection Techniques." 2012. http://hdl.handle.net/10454/8170.

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Yes<br>Emerging applications in surveillance, the entertainment industry and other human computer interaction applications have motivated the development of real-time facial analysis research covering detection, tracking and recognition. In this paper, the authors present a review of recent facial analysis for real-time applications, by providing an up-to-date review of research efforts in human computing techniques in the visible domain. The main goal is to provide a comprehensive reference source for researchers, regardless of specific research areas, involved in real-time facial analysis. First, the authors undertake a thorough survey and comparison in face detection techniques. In this survey, they discuss some prominent face detection methods presented in the literature. The performance of the techniques is evaluated by using benchmark databases. Subsequently, the authors provide an overview of the state-of-the-art of facial expressions analysis and the importance of psychology inherent in facial expression analysis. During the last decades, facial expressions analysis has slowly evolved into automatic facial expressions analysis due to the popularity of digital media and the maturity of computer vision. Hence, the authors review some existing automatic facial expressions analysis techniques. Finally, the authors provide an exemplar for the development of a facial analysis real-time application and propose a model for facial analysis. This review shows that facial analysis for real-time application involves multi-disciplinary aspects and it is important to take all domains into account when building a reliable system.
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CHIANG, YAN-MENG, and 江彥孟. "An empirical study on detecting fake reviews using deep learning and machine learning techniques." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/7b939e.

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碩士<br>東吳大學<br>資訊管理學系<br>106<br>The increasing share of the online businesses in market economy has led to a larger influence and importance of the online reviews. Before making a purchase, users are increasingly inclined to browse online forum that are posted to share post-purchase experiences of products and services. However, there are many fake reviews in the real world, consumers can't identify authentic and fake reviews. Fake online shopping reviews are harmful to consumers who might buy misrepresented products. Therefore, we proposed a framework which could detect fake reviews. In this study, we focused on the data on the web forum called Mobile01 and used text mining to deal with textual data including Bag-of-words, Latent Semantic Analysis and word2vec for word representation. Next, we used machine learning to train the model to detect fake review, including SVM, Deep Neural Network (DNN), Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM). Finally, we chose three best performance models to vote and hope that these fake reviews samples can be the reference in the future research.
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Palanisamy, Sundar Agnideven. "Learning-based Attack and Defense on Recommender Systems." Thesis, 2021. http://dx.doi.org/10.7912/C2/65.

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Indiana University-Purdue University Indianapolis (IUPUI)<br>The internet is the home for massive volumes of valuable data constantly being created, making it difficult for users to find information relevant to them. In recent times, online users have been relying on the recommendations made by websites to narrow down the options. Online reviews have also become an increasingly important factor in the final choice of a customer. Unfortunately, attackers have found ways to manipulate both reviews and recommendations to mislead users. A Recommendation System is a special type of information filtering system adapted by online vendors to provide suggestions to their customers based on their requirements. Collaborative filtering is one of the most widely used recommendation systems; unfortunately, it is prone to shilling/profile injection attacks. Such attacks alter the recommendation process to promote or demote a particular product. On the other hand, many spammers write deceptive reviews to change the credibility of a product/service. This work aims to address these issues by treating the review manipulation and shilling attack scenarios independently. For the shilling attacks, we build an efficient Reinforcement Learning-based shilling attack method. This method reduces the uncertainty associated with the item selection process and finds the most optimal items to enhance attack reach while treating the recommender system as a black box. Such practical online attacks open new avenues for research in building more robust recommender systems. When it comes to review manipulations, we introduce a method to use a deep structure embedding approach that preserves highly nonlinear structural information and the dynamic aspects of user reviews to identify and cluster the spam users. It is worth mentioning that, in the experiment with real datasets, our method captures about 92\% of all spam reviewers using an unsupervised learning approach.
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(11190282), Agnideven Palanisamy Sundar. "Learning-based Attack and Defense on Recommender Systems." Thesis, 2021.

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The internet is the home for massive volumes of valuable data constantly being created, making it difficult for users to find information relevant to them. In recent times, online users have been relying on the recommendations made by websites to narrow down the options. Online reviews have also become an increasingly important factor in the final choice of a customer. Unfortunately, attackers have found ways to manipulate both reviews and recommendations to mislead users. A Recommendation System is a special type of information filtering system adapted by online vendors to provide suggestions to their customers based on their requirements. Collaborative filtering is one of the most widely used recommendation systems; unfortunately, it is prone to shilling/profile injection attacks. Such attacks alter the recommendation process to promote or demote a particular product. On the other hand, many spammers write deceptive reviews to change the credibility of a product/service. This work aims to address these issues by treating the review manipulation and shilling attack scenarios independently. For the shilling attacks, we build an efficient Reinforcement Learning-based shilling attack method. This method reduces the uncertainty associated with the item selection process and finds the most optimal items to enhance attack reach while treating the recommender system as a black box. Such practical online attacks open new avenues for research in building more robust recommender systems. When it comes to review manipulations, we introduce a method to use a deep structure embedding approach that preserves highly nonlinear structural information and the dynamic aspects of user reviews to identify and cluster the spam users. It is worth mentioning that, in the experiment with real datasets, our method captures about 92\% of all spam reviewers using an unsupervised learning approach.<br>
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Books on the topic "Fake review detection"

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Bone Gap. Faber & Faber, Limited, 2017.

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Ruby, Laura. Bone Gap. HarperCollins Publishers, 2016.

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Bone Gap. Balzer + Bray, 2016.

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Bone Gap. Balzer + Bray, an imprint of HarperCollinsPublishers, 2015.

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Ruby, Laura. Bone Gap. HarperCollins Publishers, 2015.

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Book chapters on the topic "Fake review detection"

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Bisht, Aditya S., Manish M. Tripathi, and Faiyaz Ahmad. "Opinion Spamming: Fake Consumer Review Detection." In Computer Vision and Robotics. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-8225-4_24.

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Möhring, Michael, Barbara Keller, Rainer Schmidt, Matthias Gutmann, and Scott Dacko. "HOTFRED: A Flexible Hotel Fake Review Detection System." In Information and Communication Technologies in Tourism 2021. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-65785-7_29.

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AbstractThe importance to cope with online fake reviews in Tourism becomes more and more evident. In the hotel sector hoteliers as well as guests often struggle with the challenges to separate true and fake reviews from each other. Therefore, our research introduces HOTFRED - a flexible hotel fake review detection system - as part of an on-going research project. By combining different analytical approaches, the HOTFRED system indicates via an aggregated probability whether a review is true or fake. As the evaluation of the prototypical implementation showed, this approach can support to detect fake reviews. Many different stakeholders in the Tourism sector can profit from this automatic tool. Thus, hoteliers can take measures to safe their reputation, guests can benefit in their decision-making process and research might use the tool as an initial starting point for future research in the area of fake information.
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Hegde, Sindhu, Raghu Raj Rai, P. G. Sunitha Hiremath, and Shankar Gangisetty. "Fake Review Detection Using Hybrid Ensemble Learning." In Lecture Notes in Electrical Engineering. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-6987-0_22.

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Sushma, K., and M. Neeladri. "Fake News Identification and Detection: A Brief Review." In Smart Innovation, Systems and Technologies. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-0108-9_39.

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Gupta, Anunay, Anjum Anjum, Shreyansh Gupta, and Rahul Katarya. "Recent Trends of Fake News Detection: A Review." In Lecture Notes in Electrical Engineering. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-2354-7_43.

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Jain, Mayank Kumar, Ritika Garg, Dinesh Gopalani, and Yogesh Kumar Meena. "Review on Analysis of Classifiers for Fake News Detection." In Communications in Computer and Information Science. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-07012-9_34.

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Jadhav, Yogesh, and Deepa Parasar. "Fake Review Detection System Through Analytics of Sales Data." In Proceeding of First Doctoral Symposium on Natural Computing Research. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-4073-2_1.

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Vidanagama, Dushyanthi Udeshika, Thushari Silva, and Asoka Karunananda. "Content Related Feature Analysis for Fake Online Consumer Review Detection." In Computer Networks, Big Data and IoT. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-0965-7_35.

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Barrutia-Barreto, Israel, Renzo Seminario-Córdova, and Brian Chero-Arana. "Fake News Detection in Internet Using Deep Learning: A Review." In Studies in Computational Intelligence. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-90087-8_3.

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Tanwar, Vidhu, and Kapil Sharma. "A Review on Enhanced Techniques for Multimodal Fake News Detection." In Lecture Notes in Electrical Engineering. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-8297-4_61.

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Conference papers on the topic "Fake review detection"

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Santhosh Krishna, B. V., Sanjeev Sharma, K. Devika, Y. Sahana, K. N. Sharanya, and C. Indraja. "Review of Fake Product Review Detection Techniques." In 2022 Second International Conference on Artificial Intelligence and Smart Energy (ICAIS). IEEE, 2022. http://dx.doi.org/10.1109/icais53314.2022.9742735.

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Bhopale, Janhavi, Rugved Bhise, Arthav Mane, and Kiran Talele. "A Review-and-Reviewer based approach for Fake Review Detection." In 2021 Fourth International Conference on Electrical, Computer and Communication Technologies (ICECCT). IEEE, 2021. http://dx.doi.org/10.1109/icecct52121.2021.9616697.

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Cao, Chen, Shihao Li, Shuo Yu, and Zhikui Chen. "Fake Reviewer Group Detection in Online Review Systems." In 2021 International Conference on Data Mining Workshops (ICDMW). IEEE, 2021. http://dx.doi.org/10.1109/icdmw53433.2021.00122.

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Mohawesh, Rami, Shuxiang Xu, Matthew Springer, Muna Al-Hawawreh, and Sumbal Maqsood. "Fake or Genuine? Contextualised Text Representation for Fake Review Detection." In 10th International Conference on Natural Language Processing (NLP 2021). Academy and Industry Research Collaboration Center (AIRCC), 2021. http://dx.doi.org/10.5121/csit.2021.112311.

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Online reviews have a significant influence on customers' purchasing decisions for any products or services. However, fake reviews can mislead both consumers and companies. Several models have been developed to detect fake reviews using machine learning approaches. Many of these models have some limitations resulting in low accuracy in distinguishing between fake and genuine reviews. These models focused only on linguistic features to detect fake reviews and failed to capture the semantic meaning of the reviews. To deal with this, this paper proposes a new ensemble model that employs transformer architecture to discover the hidden patterns in a sequence of fake reviews and detect them precisely. The proposed approach combines three transformer models to improve the robustness of fake and genuine behaviour profiling and modelling to detect fake reviews. The experimental results using semi-real benchmark datasets showed the superiority of the proposed model over state-of-the-art models.
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Jnoub, Nour, and Wolfgang Klas. "Declarative Programming Approach for Fake Review Detection." In 2020 15th International Workshop on Semantic and Social Media Adaptation and Personalization (SMAP). IEEE, 2020. http://dx.doi.org/10.1109/smap49528.2020.9248468.

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Hassan Sohan, Md Mahadi, Mohammad Monirujjaman Khan, Ipseeta Nanda, and Rajesh Dey. "Fake Product Review Detection Using Machine Learning." In 2022 IEEE World AI IoT Congress (AIIoT). IEEE, 2022. http://dx.doi.org/10.1109/aiiot54504.2022.9817271.

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Deng, Huaxun, Linfeng Zhao, Ning Luo, et al. "Semi-Supervised Learning Based Fake Review Detection." In 2017 IEEE International Symposium on Parallel and Distributed Processing with Applications and 2017 IEEE International Conference on Ubiquitous Computing and Communications (ISPA/IUCC). IEEE, 2017. http://dx.doi.org/10.1109/ispa/iucc.2017.00195.

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8

Kumar, P. Manish, S. Shri Harrsha, K. Abhiram, M. Kavitha, and M. Kalyani. "Role of Machine Learning in Fake Review Detection." In 2022 6th International Conference on Electronics, Communication and Aerospace Technology (ICECA). IEEE, 2022. http://dx.doi.org/10.1109/iceca55336.2022.10009174.

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Rout, Jitendra Kumar, Amiya Kumar Dash, and Niranjan Kumar Ray. "A Framework for Fake Review Detection: Issues and Challenges." In 2018 International Conference on Information Technology (ICIT). IEEE, 2018. http://dx.doi.org/10.1109/icit.2018.00014.

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Romanov, Aleksei, Alexander Semenov, Oleksiy Mazhelis, and Jari Veijalainen. "Detection of Fake Profiles in Social Media - Literature Review." In 13th International Conference on Web Information Systems and Technologies. SCITEPRESS - Science and Technology Publications, 2017. http://dx.doi.org/10.5220/0006362103630369.

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