Fake Reviews Detection: A Survey

Mohawesh, Rami; Xu, Shuxiang; Tran, Son N.; Ollington, Robert; Springer, Matthew; Jararweh, Yaser; Maqsood, Sumbal

doi:10.1109/access.2021.3075573

Cited by 91 publications

(23 citation statements)

References 137 publications

(187 reference statements)

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“…Machine Learning comes with many classification algorithms. In a recent study [21], they came up with a survey report and concluded that machine learning algorithms perform well on medium-sized datasets as compared to Artificial Neural Network (ANN) and deep learning models, which are a good fit for large size datasets. Feature engineering is another reason ANN and deep learning models do not show better results for fake review detection.…”

Section: B Classification Algorithmsmentioning

confidence: 99%

“…Moreover, fake review detection is more challenging with realistic datasets with overlapping between legitimate and fake review data. [21].…”

Section: Experimentation Design / Detailsmentioning

confidence: 99%

“…Therefore, it is vital to determine their authenticity by using Machine Learning techniques such as K-Nearest Neighbor, Support Vector Machine, and Logistic regression (SKL).In a recent study,mohawesh et.al. [21] presented a survey of existing models for fake reviews detection. According to this survey, SKL algorithms outperform the accuracy for the proposed problem.…”

Section: Introductionmentioning

confidence: 99%

“…The Naive Bayes algorithm is one of the best classification algorithms of machine learning. However, the accuracy of the Naive Bayes algorithm for the detection of fake reviews is slightly less than SKL algorithms [21]. The proposed system includes the following modules;…”

Section: Introductionmentioning

confidence: 99%

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The Effect of Fake Reviews on e-Commerce During and After Covid-19 Pandemic: SKL-Based Fake Reviews Detection

Tufail

Ashraf²,

Alsubhi

et al. 2022

IEEE Access

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The outbreak of Covid-19 and the enforcement of lockdown, social distancing, and other precautionary measures lead to a global increase in online shopping. The increasing significance of online shopping and extensive use of e-commerce has increased competition between companies for online selling. Highlights that online reviews play a significant role in boosting a business or slandering it. Product review is an essential factor in customers' decision-making, leading to an intense topic known as fraudulent or fake reviews detection. Given these reviews' power over a business, the treacherous acts of giving false reviews for personal gains have increased with time. In our research, we proposed a fake review detection model by using Text Classification and techniques related to Machine Learning. We used classifiers such as Support Vector Machine, K-Nearest Neighbor, and logistic regression (SKL), using a bigram model that detects fraudulent reviews based on the number of pronouns, verbs, and sentiments. Our proposed methodology for detecting fake online reviews outperforms on the yelp dataset and the TripAdvisor dataset compared to other state-of-the-art techniques with 95% and 89.03% accuracy.INDEX TERMS Fake reviews, K-Nearest Neighbor (KNN), machine learning, natural language processing, sentiment analysis, support vector machine (SVM).

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Section: B Classification Algorithmsmentioning

confidence: 99%

“…Moreover, fake review detection is more challenging with realistic datasets with overlapping between legitimate and fake review data. [21].…”

Section: Experimentation Design / Detailsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The Effect of Fake Reviews on e-Commerce During and After Covid-19 Pandemic: SKL-Based Fake Reviews Detection

Tufail

Ashraf²,

Alsubhi

et al. 2022

IEEE Access

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“…Apart from detecting such reviews, identifying spamming individuals and their possible associations have also been pursued. Machine learning and graph-based approaches have been explored too [4]- [6]. A major problem in this field is the lack of a standard dataset.…”

Section: Introductionmentioning

confidence: 99%

Detecting Product Review Spammers Using Principles of Big Data

Rout

Dalmia²,

Rath

et al. 2023

IEEE Trans. Eng. Manage.

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Growing consumerism has led to the importance of online reviews on the Internet. Opinions voiced by these reviews are taken into consideration by many consumers for making financial decisions online. This has led to the development of opinion spamming for profitable motives or otherwise. Work has been done to tackle the challenge of identifying such spammers, but the scale of the real-world review systems demands this problem to be tackled as a Big Data challenge. So, an effort has been made to detect online review spammers using the principle of Big Data. In this work, a rating-based model has been studied under the light of large-scale datasets (more than 80 million reviews by 20 million reviewers) using the Hadoop and Spark frameworks. Scale effects have been identified and mitigated to provide better context to large review systems. An improved computational framework has been presented to compute the overall spamcity of reviewers using exponential smoothing. The value of the smoothing factor was set empirically. Finally, future directions have been discussed.

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Can end‐user feedback in social media be trusted for software evolution: Exploring and analyzing fake reviews

Khan,

Ullah,

Khan

et al. 2023

Concurrency and Computation

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SummaryEnd‐user feedback in social media platforms, particularly in the app stores, is increasing exponentially with each passing day. Software researchers and vendors started to mine end‐user feedback by proposing text analytics methods and tools to extract useful information for software evolution and maintenance. In addition, research shows that positive feedback and high‐star app ratings attract more users and increase downloads. However, it emerged in the fake review market, where software vendors started incorporating fake reviews against their corresponding applications to improve overall software ratings. For this purpose, we conducted an exploratory study to understand how end‐users register and write fake reviews in the Google Play Store. We curated a research data set containing 68,000 end‐user comments from the Google Play Store and a fake review generator, that is, the Testimonial generator (TG). Its purpose is to understand fake reviews on these platforms and identify the common patterns potential end‐users and professionals use to report fake reviews by critically analyzing the end‐user feedback. We conducted a detailed survey at the University of Science and Technology Bannu, Pakistan, to identify the intelligence and accuracy of crowd‐users in manually identifying fake reviews. In addition, we developed a ground truth to be compared with the results obtained from the automated machine and deep learning (M&DL) classifier experiment. In the survey, 512 end‐users participated and recorded their responses in identifying fake reviews. Finally, various M&DL classifiers are employed to classify and identify end‐user reviews into real and fake to automate the process. Unlike humans, the M&DL classifiers performed well in automatically classifying reviews into real and fake by obtaining much higher accuracy, precision, recall, and f‐measures. The accuracy of manually identifying fake reviews by the crowd‐users is 44.4%. In contrast, the M&DL classifiers obtained an average accuracy of 96%. The experimental results obtained with various M&DL classifiers are encouraging. It is the first step towards identifying fake reviews in the app store by studying its implications in software and requirements engineering.

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Fake Reviews Detection: A Survey

Cited by 91 publications

References 137 publications

The Effect of Fake Reviews on e-Commerce During and After Covid-19 Pandemic: SKL-Based Fake Reviews Detection

The Effect of Fake Reviews on e-Commerce During and After Covid-19 Pandemic: SKL-Based Fake Reviews Detection

Detecting Product Review Spammers Using Principles of Big Data

Can end‐user feedback in social media be trusted for software evolution: Exploring and analyzing fake reviews

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