A Method for the Detection of Fake Reviews Based on Temporal Features of Reviews and Comments

Liu, Wenqian; He, Jingsha; Han, Sunyoung; Cai, Fangbo; Yang, Zhenning; Zhu, Nafei

doi:10.1109/emr.2019.2928964

Cited by 38 publications

(6 citation statements)

References 25 publications

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“…Accordingly, detecting spam reviews is challenging due to the variety of spamming techniques used by spammers; hence, researchers have proposed various approaches for spam review detection (Wu et al, 2018). These techniques are based on ml methods (Albayati and Altamimi, 2019;Liu et al, 2019;Sun et al, 2022) and social network analysis (Liu et al, 2016;Sun et al, 2022). A representative example of the latter is the work by Rathore et al (2021) on fake reviewer group detection.…”

Section: Related Workmentioning

confidence: 99%

“…It can be obtained using linguistic and semantic knowledge or style analysis via nlp approaches. User-based profiling focuses on both the demographic and the behavioral activity of the user (Miller et al, 2014;Eshraqi et al, 2015;Liu et al, 2016Liu et al, , 2019Sun et al, 2022). It contemplates demography information, frequency, timing, and content of posts to distinguish legitimate from spammer users.…”

Section: Profiling and Classificationmentioning

confidence: 99%

See 1 more Smart Citation

Online Detection and Infographic Explanation of Spam Reviews with Data Drift Adaptation

de Arriba-Pérez,

García-Méndez,

Leal

et al. 2024

Informatica

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Spam reviews are a pervasive problem on online platforms due to its significant impact on reputation. However, research into spam detection in data streams is scarce. Another concern lies in their need for transparency. Consequently, this paper addresses those problems by proposing an online solution for identifying and explaining spam reviews, incorporating data drift adaptation. It integrates (i) incremental profiling, (ii) data drift detection & adaptation, and (iii) identification of spam reviews employing Machine Learning. The explainable mechanism displays a visual and textual prediction explanation in a dashboard. The best results obtained reached up to 87 % spam F-measure.

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Section: Related Workmentioning

confidence: 99%

Section: Profiling and Classificationmentioning

confidence: 99%

Online Detection and Infographic Explanation of Spam Reviews with Data Drift Adaptation

de Arriba-Pérez,

García-Méndez,

Leal

et al. 2024

Informatica

View full text Add to dashboard Cite

show abstract

“…The Trust level of the user, activity during the review process, and social and personal behavior of the user need to be analyzed along with textual review for better results. Reviews and comments given to the product have been analyzed for detection of an outlier review in [23]. Another kind of new model for fake review detection is based on the semantic and emotional level of the reviewer as well as the density of reviews, which gives a much better performance than the traditional method, which is based on reviewer info, behavior, and textual review [24].…”

Section: Case Study Through Fake Review Analysismentioning

confidence: 99%

Enhancing Data Integrity in Mobile Crowdsensing Environment with Machine Learning and Cost-Benefit Analysis

Sahoo¹,

Pradhan²,

Sethi³

et al. 2023

IJCDS

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Mobile Crowdsensing (MCS) is a major source of a vast dataset containing heterogeneous types of data collected from various sources and stored in the local or remote server. Proper analysis of MCS data helps in better decision-making. However, MCS data suffers from data integrity issues, such as validity, accuracy, and reliability, that affect decision-making. Therefore, ensuring data integrity in the MCS environment is essential as it is a major source of a huge dataset. The proposed work considers user review data collection and analysis using a mobile application developed for the purpose. To ensure the data integrity, identification of fake and invalid reviews in the dataset need to be determined. This work proposes two approaches to solve data integrity issues. The first approach is to detect and eliminate fake/ invalid reviews from the dataset. The second is to identify the sources of fake/ invalid reviews and block them to protect the dataset from future fake reviews. Machine learning (ML) models are proposed to solve these issues and to ensure data integrity by filtering out fake reviews from real-time data sets. The proposed model uses data fuzzification over a purely mathematical model that categorizes users or customers as honest, suspicious, or malicious and their reviews/ feedback as genuine or fake using ratings provided by the user in the MCS Environment. Using the developed mobile application, user can give feedback about the desired location through various devices, which is stored in a cloud platform. The dataset can be analyzed through a fuzzy logic-based mathematical model followed by an ML algorithm and cost-benefit analysis to detect genuine reviews for maintaining data integrity. Further accuracy of the proposed models is compared with popular ML algorithms such as Naive Bayes (NB), Bayes Net(BN), Support Vector Machine(SVM), Decision Tree(J48), and Random Forest(RF). Initially, it achieves 99.79% of accuracy using the Random Forest algorithm that has been enhanced to 100% using cost-benefit analysis in cross-validation mode.

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“…Liu et al [4] introduced a method for identifying fake reviews based on product-related review data. Here, they first extract the product review records to a temporal feature vector and then create an isolation forest algorithm to detect the external reviews by concentrating on the difference between the product review trends to classify the outlier reviews.…”

Section: Temporal Features-based Detection Modelsmentioning

confidence: 99%

“…With the increasing importance of online reviews, the tendency to manipulate customers by posting fake/false reviews has also increased drastically. Many organizations use fake reviews as a tool to boost their product sales, and many use them to drop the value of other organizations [3,4]. These reviews are posted by either a single spammer or a group of spammers hired by an organization/company to manipulate customers.…”

Section: Introductionmentioning

confidence: 99%

SAFER: Sentiment Analysis-Based FakE Review Detection in E-Commerce Using Deep Learning

et al. 2021

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The problem of fake deceptive reviews has become a threatening aspect for online users in recent years. With the evolution of the online markets, the trend towards fake reviews has increased, mainly to attract or distract customers. Fake reviews have affected both customers and sellers. These reviews consist of writings and spreading misleading information and beliefs. Sentiment analysis was first introduced a few years ago in the e-commerce sector. It is an emerging research area today due to the rapid growth in the e-commerce industry. The biggest challenge in detecting fake reviews is the lack of an effective way to distinguish fake reviews from legitimate reviews. The difference cannot be seen with the naked eye and is, therefore, a severe concern. In this paper, we have applied the bag of words model and glove embedding matrix with a focus on fake reviews. We have used two different feature extraction techniques and three new deep-learning algorithms on text classifications. The experimental analysis with an existing public dataset showed good and better results compared to the traditional machine-learning models.

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A Method for the Detection of Fake Reviews Based on Temporal Features of Reviews and Comments

Cited by 38 publications

References 25 publications

Online Detection and Infographic Explanation of Spam Reviews with Data Drift Adaptation

Online Detection and Infographic Explanation of Spam Reviews with Data Drift Adaptation

Enhancing Data Integrity in Mobile Crowdsensing Environment with Machine Learning and Cost-Benefit Analysis

SAFER: Sentiment Analysis-Based FakE Review Detection in E-Commerce Using Deep Learning

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