Journal articles: '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 (June 15, 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 (July 31, 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 (April 30, 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 (April 30, 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 (May 31, 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 (March 31, 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 (July 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 (August 31, 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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Alvi, Muhammad Bux, Majdah Alvi, Rehan Ali Shah, Mubashira Munir, and Adnan Akhtar. "Machine learning-based Fake reviews detection with amalgamated features extraction method." Sukkur IBA Journal of Emerging Technologies 5, no. 2 (January 7, 2023): 10–17. http://dx.doi.org/10.30537/sjet.v5i2.1091.

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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.

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Krishnan, Kavita, and Yun Wan. "The Detection of Fake Reviews in Bestselling Books." Journal of Electronic Commerce in Organizations 19, no. 4 (October 2021): 64–79. http://dx.doi.org/10.4018/jeco.2021100104.

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This study detected the possible manipulation of reviews for bestseller books. The authors first used clustering analysis to identify the cluster of bestselling books and patterns of manipulated reviews and ratings. They then used an artificial neural network to predict the possibility of review manipulation in bestselling books based on the patterns identified. The prediction outcome has an accuracy rate of 89%. They found that fake or manipulated reviews for bestselling books could be identified by analyzing abnormal rating fluctuations. The findings could help e-commerce platforms identify review manipulations and thereby help customers make prudent purchase decisions.

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Abhinandan V., Aishwarya C. A., and Arshiya Sultana. "Fake Review Detection Using Machine Learning Techniques." International Journal of Fog Computing 3, no. 2 (July 2020): 46–54. http://dx.doi.org/10.4018/ijfc.2020070104.

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Online reviews play a vital role in today's business and commerce. In the world of e-commerce, reviews are the best signs of success and failure. Businesses that have good reviews get a lot of free exposure on websites and pages that have good reviews show up at the top of the search results. Fake reviews are everywhere online. Online fake reviews are the reviews which are written by someone who has not actually used the product or the services. Because of the cut-throat competition, sellers are now willing to resort to unfair means to make their product stand out. This work introduces some supervised machine learning techniques to detect fake online reviews and also be able to block the malicious users who post such reviews.

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Fang, Youli, Hong Wang, Lili Zhao, Fengping Yu, and Caiyu Wang. "Dynamic knowledge graph based fake-review detection." Applied Intelligence 50, no. 12 (July 15, 2020): 4281–95. http://dx.doi.org/10.1007/s10489-020-01761-w.

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Soldner, Felix, Bennett Kleinberg, and Shane D. Johnson. "Confounds and overestimations in fake review detection: Experimentally controlling for product-ownership and data-origin." PLOS ONE 17, no. 12 (December 7, 2022): e0277869. http://dx.doi.org/10.1371/journal.pone.0277869.

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The popularity of online shopping is steadily increasing. At the same time, fake product reviews are published widely and have the potential to affect consumer purchasing behavior. In response, previous work has developed automated methods utilizing natural language processing approaches to detect fake product reviews. However, studies vary considerably in how well they succeed in detecting deceptive reviews, and the reasons for such differences are unclear. A contributing factor may be the multitude of strategies used to collect data, introducing potential confounds which affect detection performance. Two possible confounds are data-origin (i.e., the dataset is composed of more than one source) and product ownership (i.e., reviews written by individuals who own or do not own the reviewed product). In the present study, we investigate the effect of both confounds for fake review detection. Using an experimental design, we manipulate data-origin, product ownership, review polarity, and veracity. Supervised learning analysis suggests that review veracity (60.26–69.87%) is somewhat detectable but reviews additionally confounded with product-ownership (66.19–74.17%), or with data-origin (84.44–86.94%) are easier to classify. Review veracity is most easily classified if confounded with product-ownership and data-origin combined (87.78–88.12%). These findings are moderated by review polarity. Overall, our findings suggest that detection accuracy may have been overestimated in previous studies, provide possible explanations as to why, and indicate how future studies might be designed to provide less biased estimates of detection accuracy.

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Tang, Haoxing, and Hui Cao. "A review of research on detection of fake commodity reviews." Journal of Physics: Conference Series 1651 (November 2020): 012055. http://dx.doi.org/10.1088/1742-6596/1651/1/012055.

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Mukherjee, Arjun, Vivek Venkataraman, Bing Liu, and Natalie Glance. "What Yelp Fake Review Filter Might Be Doing?" Proceedings of the International AAAI Conference on Web and Social Media 7, no. 1 (August 3, 2021): 409–18. http://dx.doi.org/10.1609/icwsm.v7i1.14389.

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Online reviews have become a valuable resource for decision making. However, its usefulness brings forth a curse ‒ deceptive opinion spam. In recent years, fake review detection has attracted significant attention. However, most review sites still do not publicly filter fake reviews. Yelp is an exception which has been filtering reviews over the past few years. However, Yelp’s algorithm is trade secret. In this work, we attempt to find out what Yelp might be doing by analyzing its filtered reviews. The results will be useful to other review hosting sites in their filtering effort. There are two main approaches to filtering: supervised and unsupervised learning. In terms of features used, there are also roughly two types: linguistic features and behavioral features. In this work, we will take a supervised approach as we can make use of Yelp’s filtered reviews for training. Existing approaches based on supervised learning are all based on pseudo fake reviews rather than fake reviews filtered by a commercial Web site. Recently, supervised learning using linguistic n-gram features has been shown to perform extremely well (attaining around 90% accuracy) in detecting crowdsourced fake reviews generated using Amazon Mechanical Turk (AMT). We put these existing research methods to the test and evaluate performance on the real-life Yelp data. To our surprise, the behavioral features perform very well, but the linguistic features are not as effective. To investigate, a novel information theoretic analysis is proposed to uncover the precise psycholinguistic difference between AMT reviews and Yelp reviews (crowdsourced vs. commercial fake reviews). We find something quite interesting. This analysis and experimental results allow us to postulate that Yelp’s filtering is reasonable and its filtering algorithm seems to be correlated with abnormal spamming behaviors.

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Li, Huayi, Zhiyuan Chen, Arjun Mukherjee, Bing Liu, and Jidong Shao. "Analyzing and Detecting Opinion Spam on a Large-scale Dataset via Temporal and Spatial Patterns." Proceedings of the International AAAI Conference on Web and Social Media 9, no. 1 (August 3, 2021): 634–37. http://dx.doi.org/10.1609/icwsm.v9i1.14652.

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Although opinion spam (or fake review) detection has attracted significant research attention in recent years, the problem is far from solved. One key reason is that there is no large-scale ground truth labeled dataset available for model building. Some review hosting sites such as Yelp.com and Dianping.com have built fake review filtering systems to ensure the quality of their reviews, but their algorithms are trade secrets. Working with Dianping, we present the first large-scale analysis of restaurant reviews filtered by Dianping's fake review filtering system. Along with the analysis, we also propose some novel temporal and spatial features for supervised opinion spam detection. Our results show that these features significantly outperform existing state-of-art features.

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Kewlani, Dinkal. "A Review Paper on Fake News Detection Techniques." International Journal for Research in Applied Science and Engineering Technology 9, no. 4 (April 30, 2021): 488–93. http://dx.doi.org/10.22214/ijraset.2021.33633.

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Goyal, Priyanshi, Dr Swapnesh Taterh, and Mr Ankit Saxena. "Fake News Detection using Machine Learning: A Review." International Journal of Advanced Engineering, Management and Science 7, no. 3 (2021): 33–38. http://dx.doi.org/10.22161/ijaems.73.6.

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Dwivedi, Mahendra Nath. "Detection of Fake Users Account Based on Review." International Journal for Research in Applied Science and Engineering Technology 6, no. 6 (June 30, 2018): 20–24. http://dx.doi.org/10.22214/ijraset.2018.6005.

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Jnoub, Nour, Admir Brankovic, and Wolfgang Klas. "Fact-Checking Reasoning System for Fake Review Detection Using Answer Set Programming." Algorithms 14, no. 7 (June 24, 2021): 190. http://dx.doi.org/10.3390/a14070190.

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A rising number of people use online reviews to choose if they want to use or buy a service or product. Therefore, approaches for identifying fake reviews are in high request. This paper proposes a hybrid rule-based fact-checking framework based on Answer Set Programming (ASP) and natural language processing. The paper incorporates the behavioral patterns of reviewers combined with the qualitative and quantitative properties/features extracted from the content of their reviews. As a case study, we evaluated the framework using a movie review dataset, consisting of user accounts with their associated reviews, including the review title, content, and the star rating of the movie, to identify reviews that are not trustworthy and labeled them accordingly in the output. This output is then used in the front end of a movie review platform to tag reviews as fake and show their sentiment. The evaluation of the proposed approach showed promising results and high flexibility.

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Feng, Yuran. "Misreporting and Fake News Detection Techniques on the Social Media Platform." Highlights in Science, Engineering and Technology 12 (August 26, 2022): 142–52. http://dx.doi.org/10.54097/hset.v12i.1417.

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One of the major concerns nowadays is the rapid spreading of fake news or unverified information on all kinds of social media. Misinformation and disinformation on the digital media of news distribution have brought significant negative impacts to our community, which the traditional techniques can no longer detect and deal with it effectively. It is urgent to squelch fake news immediately to limit its impact on the economy and society. As deep learning techniques continue developing in recent decades, scholars successfully deployed deep neural networks on fake news detection tasks. The first noticeable thing is to admit that the fake news detection task has made significant accomplishments as fast as we hoped. It is necessary to study further and broadly review the state-of-the-art fake news detection approaches. In this review paper, we survey several distinct deep learning techniques and provide a comprehensive review of automatic fake news detection classification tasks and the datasets and models used, demonstrating the performance evaluation on different approaches. We have also analyzed the potential challenge we encountered in fake news detection.

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Jing-Yu, Chen, and Wang Ya-Jun. "Semi-Supervised Fake Reviews Detection based on AspamGAN." March 2022 4, no. 1 (March 30, 2022): 17–36. http://dx.doi.org/10.36548/jaicn.2022.1.002.

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With the popularization of social software and e-business in recent years, more and more consumers like to share their consumption experiences on social networks and refer to other consumers' reviews and opinions when making consumption decisions. Online reviews have become an essential part of browsing on websites such as shopping, and people's reliance on informative reviews have contributed to the rise of fake reviews. The traditional classification method is affected by the label dataset, which is not only time-consuming, laborious, and subjective, but also the extraction of artificial features also affects the classification accuracy. Due to the relative length of the online text, the possibility of the classifier losing important information increases, this weakens the model’s detection capability. To solve this aforementioned problem, a semi-supervised Generative Adversarial Network (AspamGAN) fake reviews detection method incorporating an attention mechanism is proposed. Using labeled and unlabeled data to correctly learn input distributions, the features required for classification are automatically discovered using deep neural networks, providing better prediction accuracy for online reviews. The approach includes attention mechanisms in the classifier to obtain an adequate semantic representation and relies on a limited dataset of labeled data to detect false reviews, and is applied on the TripAdvisor dataset. Experimental results show that the proposed algorithm outperforms state-of-the-art semi-supervised fake review detection techniques when the label dataset is limited.

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B. A, Mr Abhale, Miss Bachhav M. K., and Miss Patil Y. P. "Fake Reviews Detection Using Multidimensional Representations with Fine-Grained Aspects Plan." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (May 31, 2022): 2613–18. http://dx.doi.org/10.22214/ijraset.2022.42581.

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Abstract: As the trend to shop online is growing day by day and lot of people are interested in purchasing the products of their need from the online stores. This way of shopping does not take a lot of time of a customer. In this case reviews on online websites play a important role in sales of the product because people try to get all the pros and cons of any product before they buy it. Most of the people needs genuine information about the product while online shopping. Before spending their money on particular product can analyse the various comments in the website. In this scenario, they did not recognize whether it may be fake or genuine. Customer place the order for particular product only by considering the reviews of that product. Here, it might be possible that reviews are fake. Now here query is which are fake reviews? Fake reviews may be good or bad compliment on the products. To detect such type of reviews we have developed the system. In this research, the dataset of different fake reviews provided by Flipkart are considered where reviews sentiments are included and using the LOGISTIC REGRESSION CLASSIFIER the reviews are classified into two categories i.e. fake and genuine. So user can save his/her time only by reading genuine reviews and gives accuracy about the product. Keywords: Fake reviews, review sentiment, logistic regression classifier, detection, feature extraction, web scrapping.

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Dwivedi, Mahendra Nath. "Review on Various Fake Review Detection Techniques on E-Commerce Website." International Journal for Research in Applied Science and Engineering Technology 6, no. 6 (June 30, 2018): 25–29. http://dx.doi.org/10.22214/ijraset.2018.6006.

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D’Ulizia, Arianna, Maria Chiara Caschera, Fernando Ferri, and Patrizia Grifoni. "Fake news detection: a survey of evaluation datasets." PeerJ Computer Science 7 (June 18, 2021): e518. http://dx.doi.org/10.7717/peerj-cs.518.

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Fake news detection has gained increasing importance among the research community due to the widespread diffusion of fake news through media platforms. Many dataset have been released in the last few years, aiming to assess the performance of fake news detection methods. In this survey, we systematically review twenty-seven popular datasets for fake news detection by providing insights into the characteristics of each dataset and comparative analysis among them. A fake news detection datasets characterization composed of eleven characteristics extracted from the surveyed datasets is provided, along with a set of requirements for comparing and building new datasets. Due to the ongoing interest in this research topic, the results of the analysis are valuable to many researchers to guide the selection or definition of suitable datasets for evaluating their fake news detection methods.

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Anand, Diksha, and Kamal Gupta. "Face Spoof Detection System Based on Genetic Algorithm and Artificial Intelligence Technique: A Review." International Journal of Advanced Research in Computer Science and Software Engineering 8, no. 6 (June 30, 2018): 51. http://dx.doi.org/10.23956/ijarcsse.v8i6.722.

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Face recognition is an alternative means to authenticate a person in different applications for access control. Instead of many improvements, this method is prone to various attacks like photos, 3D masks and video replay attack. Due to these attacks, system should require a face spoof detection system. A face spoof detection systems have an ability to identify whether a face is from a real person or a fake image. Face spoofing effect the image by adding deformation in it and also degrades the image pattern quality. Face spoofing detection system automatically identifies the human face is a true face or a fake face. In today's era, face recognition method is widely used to authenticate the face (like for unlocking mobile phones etc.) and providing access to the services or facilities but some intruders use various trick to crack the authentication system by presenting the false face in front of the authentication system, so it become necessity to prevent our face authentication system from face spoofing attack. So the choice of the technique to detect the face spoofing attack should be accurate and highly efficient.

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Zaqiyah, Ana, Diana Purwitasari, and Chastine Fatichah. "Text Generation with Content and Structure-Based Preprocessing in Imbalanced Data of Product Review." International Journal of Intelligent Engineering and Systems 14, no. 1 (February 28, 2021): 516–27. http://dx.doi.org/10.22266/ijies2021.0228.48.

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Spam detection frequently categorizes product reviews as spam and non-spam. The spam reviews may contain texts of fake reviews and non-review statements describing unrelated things about products. Most of the publicly available spam reviews are labelled as fake reviews, while non-spam texts that are not fake reviews could contain non-review statements. It is crucial to notice those non-review statements since they convey misperception to consumers. Non-review statements are hardly found, and those statements of large and long texts often need to be manually labelled, which is time-consuming. Because of the rareness in finding non-review statements, there is an imbalanced condition between non-spam as a major class and spam that consists of the non-review statement as a minor class. Augmenting fake reviews to add spam texts is ineffective because they have similar content to non-spam such as some opinion words of product features. Thus, the text generation of non-review statements is preferable for adding spam texts. Some text generation issues are the frequent neural network-based methods require much learning data, and the existing pre-trained models produce texts with different contexts to non-review statements. The augmented texts should have similar content and context represented by the structure of the non-review statement. Therefore, we propose a text generation model with content and structure-based preprocessing to produce non-review statements, which is expected to overcome imbalanced data and give better spam detection results in product reviews. Structure-based preprocessing identifies the feature structures of non-opinion words from part-of-speech tags. Those features represent the context of spam reviews in unlabeled texts. Then, content-based preprocessing appoints selected topic modeling results of non-review statements from fake reviews. Our experiments resulted an improvement on the metric value of ± 0.04, called as BLEU (Bi-Lingual Evaluation Understudy) score, for the correspondence evaluation between generated and trained texts. The metric value indicates that the generated texts are not quite identical to the trained texts of non-review statements. However, those additional texts combined with the original spam texts gave better spam detection results with an increasing value of more than 40% on average recall score.

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Khokale, Prof Swati R. "A Review on Fake News Detection with Machine Learning." International Journal for Research in Applied Science and Engineering Technology 9, no. 5 (May 31, 2021): 960–62. http://dx.doi.org/10.22214/ijraset.2021.34379.

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Agarwal, Rohit, and Anand Singh Jalal. "Presentation attack detection system for fake Iris: a review." Multimedia Tools and Applications 80, no. 10 (February 1, 2021): 15193–214. http://dx.doi.org/10.1007/s11042-020-10378-7.

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Bisht, Aditya S. "A Study on Opinion Spamming: Fake Consumer Review Detection." Journal of Informatics Electrical and Electronics Engineering (JIEEE) 2, no. 2 (June 4, 2021): 1–4. http://dx.doi.org/10.54060/jieee/002.02.004.

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Online audits are the most important wellsprings of data about client feelings and are considered the columns on which the standing of an association is assembled. From a client's viewpoint, audit data is vital to settle on an appropriate choice with respect to an online buy. Surveys are for the most part thought to be a fair-minded assessment of a person's very own involvement in an item, however, the fundamental truth about these audits recounts an alternate story. Spammers abuse these audit stages unlawfully on account of impetuses engaged with composing counterfeit surveys, subsequently at-tempting to acquire a bit of leeway over contenders bringing about an unstable development of assessment spamming. This training is known as Opinion (Review) Spam, where spammers control and toxic substance surveys (i.e., making phony, untruthful, or misleading audits) for benefit or gain. It has become a typical practice for individuals to discover and to understand assessments/surveys on the Web for some reasons. For instance, in the event that one needs to purchase an item, one commonly goes to a vendor or audit site (e.g., amazon.com) to peruse a few surveys of existing clients of the item. In the event that one sees numerous positive audits of the item, one is probably going to purchase the item. Notwithstanding, in the event that one sees many negative surveys, he/she will in all probability pick another item. Positive suppositions can bring about huge monetary benefits and additionally popularities for associations and people. This, sadly, offers great motivating forces for input spam. Most of the momentum research has zeroed in on regulated learning strategies, which require named information, a shortage with regards to online survey spam. Examination of techniques for Big Data is of revenue, since there are a huge number of online audits, with a lot seriously being produced every day. Until now, we have not discovered any papers that review the im-pacts of Big Data examination for survey spam identification. The essential objective of this paper is to give a solid and far-reaching similar investigation of flow research on identifying audit spam utilizing different AI procedures and to devise a strategy for directing further examination.

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Saraswat, Khushboo. "A Review on Fake Account Detection in Social Media." International Journal for Research in Applied Science and Engineering Technology 8, no. 12 (December 31, 2020): 1002–6. http://dx.doi.org/10.22214/ijraset.2020.32627.

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V, Vidhya. "Interface for Fake Product Review Detection, Analysis and Removal." International Journal for Research in Applied Science and Engineering Technology 8, no. 5 (May 31, 2020): 2348–53. http://dx.doi.org/10.22214/ijraset.2020.5385.

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Ruan, Na, Ruoyu Deng, and Chunhua Su. "GADM: Manual fake review detection for O2O commercial platforms." Computers & Security 88 (January 2020): 101657. http://dx.doi.org/10.1016/j.cose.2019.101657.

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Ezarfelix, Juandreas, Nathannael Jeffrey, and Novita Sari. "Systematic Literature Review: Instagram Fake Account Detection Based on Machine Learning." Engineering, MAthematics and Computer Science (EMACS) Journal 4, no. 1 (February 5, 2022): 25–31. http://dx.doi.org/10.21512/emacsjournal.v4i1.8076.

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The popularity of social media continues to grow, and its dominance of the entire world has become one of the aspects of modern life that cannot be ignored. The rapid growth of social media has resulted in the emergence of ecosystem problems. Hate speech, fraud, fake news, and a slew of other issues are becoming un-stoppable. With over 1.7 billion fake accounts on social media, the losses have al-ready been significant, and removing these accounts will take a long time. Due to the growing number of Instagram users, the need for identifying fake accounts on social media, specifically in Instagram, is increasing. Because this process takes a long time if done manually by humans, we can now use machine learning to identify fake accounts thanks to the rapid development of machine learning. We can detect fake accounts on Instagram using machine learning by implementing the combination of image detection and natural language processing.

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Agarwal, Rohit, A. S. Jalal, and K. V. Arya. "A review on presentation attack detection system for fake fingerprint." Modern Physics Letters B 34, no. 05 (February 3, 2020): 2030001. http://dx.doi.org/10.1142/s021798492030001x.

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Fingerprint recognition systems are susceptible to artificial spoof fingerprint attacks, like molds manufactured from polymer, gelatin or Play-Doh. Presentation attack is an open issue for fingerprint recognition systems. In a presentation attack, synthetic fingerprint which is reproduced from a real user is submitted for authentication. Different sensors are used to capture the live and fake fingerprint images. A liveness detection system has been designed to defeat different classes of spoof attacks by differentiating the features of live and fake fingerprint images. In the past few years, many hardware- and software-based approaches are suggested by researchers. However, the issues still remain challenging in terms of robustness, effectiveness and efficiency. In this paper, we explore all kinds of software-based solution to differentiate between real and fake fingerprints and present a comprehensive survey of efforts in the past to address this problem.

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Jany Shabu, S. L., V. Netaji Subhash Chandra Bose, Venkatesh Bandaru, Sardar Maran, and J. Refonaa. "Spam and Fake Spam Message Detection Framework Using Machine Learning Algorithm." Journal of Computational and Theoretical Nanoscience 17, no. 8 (August 1, 2020): 3444–48. http://dx.doi.org/10.1166/jctn.2020.9202.

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Online reviews about the acquisition of items or administrations gave have become the primary wellspring of clients’ conclusions. So as to pick up benefit or acclaim, as a rule spam reviews are composed to advance or downgrade a couple of target items or administrations. This training is known as review spamming. In the previous barely any years, an assortment of strategies have been proposed so as to illuminate the issue of spam reviews. It is a mainstream correspondence and furthermore known as information trade media. Information could be of a book, numbers, figures or insights that are gotten to by a PC. These days, numerous individuals relies upon substance accessible in web-based social networking in their choices. Sharing of data with people groups has additionally pulled in social spammers to endeavor and spread spam messages to advance individual web logs, notices, advancements, phishing, trick, fakes, etc. The possibility that anyone will leave a review give a brilliant possibility for spammers to post spit audit with respect to item and administrations for different interests and possibilities. In this way, we propose a fake message detection system utilizing ML to recognize the spam and fake messages on the internet based life stage.

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Paul, Himangshu, and Alexander Nikolaev. "Fake review detection on online E-commerce platforms: a systematic literature review." Data Mining and Knowledge Discovery 35, no. 5 (June 18, 2021): 1830–81. http://dx.doi.org/10.1007/s10618-021-00772-6.

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Kazia, Esmeralda. "Machine learning for False Information Detection in Social Internet of Things." Fusion: Practice and Applications 10, no. 1 (2023): 38–77. http://dx.doi.org/10.54216/fpa.100103.

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By capitalizing on object relationships and local navigability, the Social Internet of Things (SIoT) is one of the burgeoning paradigms that could solve the technical challenges of conventional IoT. Because of this paradigm's capacity to combine conventional IoT with social media, it is possible to create smart objects and services with greater utility than those created using conventional IoT infrastructures. In recent years, scholars have become interested in SIoT, leading to a plethora of works examining various mechanisms for providing services and technologies within this context. In this vein, we present a comprehensive review of recent research covering important aspects of SIoT. In this research, we give a detailed justification for the function of several machine learning paradigms and provide a practical application of hitherto unexamined concerns relating to erroneous data and other social IoT. First, we give a classification of false news detection approaches and an analysis of these techniques. Second, the potential uses for detecting fake news are examined at length, including how it might be applied to the areas of fake profile detection, traffic management, bullying detection, etc. We also suggested a detailed review of the possibilities of machine learning algorithms for detecting bogus news and intervening in social networks. In our paper, we introduce categories of fake news detection methods providing a comparison between these methods. After that, the promising applications for false news detection is extensively discussed in terms of fake account detection, bot detection, bullying detection, and security and privacy of SIoT. Also, the paper contains a discussion of the potential of machine learning approaches for fake news detection and interventions in SIoT networks along with the state-of-the-art challenges, opportunities, and future search prospects. This article seeks for aiding the readers and researchers in explaining the motive and role of the different machine learning paradigms to offer them a comprehensive realization for so far unexplored issues related to false information and other scenarios of SIoT networks.

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Fang, Kairui. "Deep Learning Techniques for Fake News Detection." Highlights in Science, Engineering and Technology 16 (November 10, 2022): 511–18. http://dx.doi.org/10.54097/hset.v16i.2638.

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Social media has recently become the primary source for people to consume news. Plenty of users prefer to go to social media apps such as Twitter, Facebook, and Snapchat to obtain the latest social events and news. Meanwhile, traditional media is emulating the new media to post their news on the aforementioned apps. This prevalence is a double-edged sword, for the advantage is that users can easily gain access to the news articles they look for on social media. However, it also provides an ideal platform for fake news propagation. The spread of fake news is extremely fast on social media and can cause adverse effects in real life. The unregimented, incomplete censorship and the absence of fact-checking processes make fake news easy to propagate and hard to control. Therefore, fake news detection on social media has become a trending topic that draws tremendous attention, as shown in figure 1. Nevertheless, as pundits dig into the realm of deep learning, some of the studies utilize deep neural networks (DNN) to build frameworks that would help detect fake news. Although impressive progress on the topic has been made, the lack of a review dissertation that summarizes and synthesizes the overall development of the study would be problematic. Hence, this paper aims to summarize different models implemented in recent studies that improve the veracity of fake news detection.

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Deva Hema, D., J. Tharun, G. Arun Dev, and N. Sateesh. "A Robust False Spam Review Detection Using Deep Long Short-Term Memory (LSTM) Based Recurrent Neural Network." Journal of Computational and Theoretical Nanoscience 17, no. 8 (August 1, 2020): 3421–26. http://dx.doi.org/10.1166/jctn.2020.9198.

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Our day-to-day activity is highly influenced by development of Internet. One of the rapid growing area in Internet is E-commerce. People are eager to buy products from online sites like Amazon, embay, Flipkart etc. Customers can write reviews about the products purchased online. The purchasing of good through online has been increasing exponentially since last few years. As there is no physical contact with goods before purchasing through online, people totally rely on reviews about the product before purchasing it. Hence review plays an important role in deciding the quality of the product. There are many customers who give online reviews about the product after using it. Hence the quality of the product is decided by the reviews of the customers. Thus, detection of fake reviews has become one of the important task. The proposed system will help in finding such fake reviews about the product, so that the fake reviews can be eliminated. Therefore, the purchasing of the products will be totally based on the genuine reviews. The proposed system uses Deep Recurrent Neural Network (DRNN) to predict the fake reviews and the performance of the proposed method has compared with Naïve Bayes Algorithm. The proposed model shows good accuracy and can handle huge amount of data over the existing system.

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Muhammad, Faisal, and Sifat Ahmed. "Fake Review Detection using Principal Component Analysis and Active Learning." International Journal of Computer Applications 178, no. 48 (September 17, 2019): 42–48. http://dx.doi.org/10.5120/ijca2019919418.

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Mridha, M. F., Ashfia Jannat Keya, Md Abdul Hamid, Muhammad Mostafa Monowar, and Md Saifur Rahman. "A Comprehensive Review on Fake News Detection With Deep Learning." IEEE Access 9 (2021): 156151–70. http://dx.doi.org/10.1109/access.2021.3129329.

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Shailendra Kangle, Nupoor, Dr Rajeshwari Kannan, and Sushma Vispute. "Application Of Machine Learning Techniques For Fake Customer Review Detection." ASIAN JOURNAL OF CONVERGENCE IN TECHNOLOGY 7, no. 3 (December 20, 2021): 13–16. http://dx.doi.org/10.33130/ajct.2021v07i03.003.

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Roy, Pradeep Kumar, and Shivam Chahar. "Fake Profile Detection on Social Networking Websites: A Comprehensive Review." IEEE Transactions on Artificial Intelligence 1, no. 3 (December 2020): 271–85. http://dx.doi.org/10.1109/tai.2021.3064901.

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Panchal, Mauik, and Rutika Ghariya. "A Review On Detection of Fake News Using Various Techniques." International Journal of Computer Science and Engineering 8, no. 6 (June 25, 2021): 1–4. http://dx.doi.org/10.14445/23488387/ijcse-v8i6p101.

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He, Daojing, Menghan Pan, Kai Hong, Yao Cheng, Sammy Chan, Xiaowen Liu, and Nadra Guizani. "Fake Review Detection Based on PU Learning and Behavior Density." IEEE Network 34, no. 4 (July 2020): 298–303. http://dx.doi.org/10.1109/mnet.001.1900542.

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., Payal Patil. "A REVIEW ON FAKE BIOMETRIC DETECTION SYSTEM FOR VARIOUS APPLICATIONS." International Journal of Research in Engineering and Technology 05, no. 03 (March 25, 2016): 495–98. http://dx.doi.org/10.15623/ijret.2016.0503089.

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Fei, Geli, Arjun Mukherjee, Bing Liu, Meichun Hsu, Malu Castellanos, and Riddhiman Ghosh. "Exploiting Burstiness in Reviews for Review Spammer Detection." Proceedings of the International AAAI Conference on Web and Social Media 7, no. 1 (August 3, 2021): 175–84. http://dx.doi.org/10.1609/icwsm.v7i1.14400.

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Online product reviews have become an important source of user opinions. Due to profit or fame, imposters have been writing deceptive or fake reviews to promote and/or to demote some target products or services. Such imposters are called review spammers. In the past few years, several approaches have been proposed to deal with the problem. In this work, we take a different approach, which exploits the burstiness nature of reviews to identify review spammers. Bursts of reviews can be either due to sudden popularity of products or spam attacks. Reviewers and reviews appearing in a burst are often related in the sense that spammers tend to work with other spammers and genuine reviewers tend to appear together with other genuine reviewers. This paves the way for us to build a network of reviewers appearing in different bursts. We then model reviewers and their co-occurrence in bursts as a Markov Random Field (MRF), and employ the Loopy Belief Propagation (LBP) method to infer whether a reviewer is a spammer or not in the graph. We also propose several features and employ feature induced message passing in the LBP framework for network inference. We further propose a novel evaluation method to evaluate the detected spammers automatically using supervised classification of their reviews. Additionally, we employ domain experts to perform a human evaluation of the identified spammers and non-spammers. Both the classification result and human evaluation result show that the proposed method outperforms strong baselines, which demonstrate the effectiveness of the method.

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Journal articles on the topic 'Fake review detection'

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