Spammer group detection and diversification of customers’ reviews

Hussain, Naveed; Mirza, Hamid Turab; Ali, Abid Hmood; Iqbal, Farrukh; Hussain, Ibrar; Kaleem, Mohammad

doi:10.7717/peerj-cs.472

Cited by 7 publications

(4 citation statements)

References 35 publications

(50 reference statements)

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“…The effectiveness of the system to deal with scalability is still doubtful. After a detailed literature work, it has been observed that only a few works have been reported in the literature that deals with a huge amount of data using Big Data or Deep Learning [86], [87].…”

Section: Related Workmentioning

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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Section: Related Workmentioning

confidence: 99%

Detecting Product Review Spammers Using Principles of Big Data

Rout

Dalmia²,

Rath

et al. 2023

IEEE Trans. Eng. Manage.

View full text Add to dashboard Cite

show abstract

“…Hussain et al [20] make a significant addition to the field of spam identification in Roman Urdu scripts by providing insights and approaches for recognizing misleading product evaluations in the realm of spam review detection in Roman Urdu scripts. Similarly, Hussain et al [21,22] present a complete analysis of linguistic and spammer behavioral strategies for detecting spam reviews individually and in groups, providing useful insights and methods for efficiently addressing the problem. Duma et al [23] introduce a unique strategy that integrates review text, overall ratings, and aspect ratings to efficiently detect false reviews, highlighting the potential of a deep hybrid model in enhancing the accuracy of fake review detection.…”

Section: Literature Reviewmentioning

confidence: 99%

Policy-Based Spam Detection of Tweets Dataset

et al. 2023

Self Cite

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Spam communications from spam ads and social media platforms such as Facebook, Twitter, and Instagram are increasing, making spam detection more popular. Many languages are used for spam review identification, including Chinese, Urdu, Roman Urdu, English, Turkish, etc.; however, there are fewer high-quality datasets available for Urdu. This is mainly because Urdu is less extensively used on social media networks such as Twitter, making it harder to collect huge volumes of relevant data. This paper investigates policy-based Urdu tweet spam detection. This study aims to collect over 1,100,000 real-time tweets from multiple users. The dataset is carefully filtered to comply with Twitter’s 100-tweet-per-hour limit. For data collection, the snscrape library is utilized, which is equipped with an API for accessing various attributes such as username, URL, and tweet content. Then, a machine learning pipeline consisting of TF-IDF, Count Vectorizer, and the following machine learning classifiers: multinomial naïve Bayes, support vector classifier RBF, logical regression, and BERT, are developed. Based on Twitter policy standards, feature extraction is performed, and the dataset is separated into training and testing sets for spam analysis. Experimental results show that the logistic regression classifier has achieved the highest accuracy, with an F1-score of 0.70 and an accuracy of 99.55%. The findings of the study show the effectiveness of policy-based spam detection in Urdu tweets using machine learning and BERT layer models and contribute to the development of a robust Urdu language social media spam detection method.

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“…Hussain et al [15], the DSR (Diversified Set of Reviews) technique, which chooses a diversified set of top-k evaluations with negative, positive, and unbiased reviews, was proposed by Hussainet al English and Roman Urdu real-world datasets are used for evaluation. The study suggests using Spammer Group Detection (SGD) identifies spam groups that may be questionable.…”

Section: Related Workmentioning

confidence: 99%

“…To check the effectiveness of GrFrauder, we compare three graph-based approaches. They are SGD [15], GGSpam [18], GSCPM [11]. In which GGSpam, GSCPM and SGD were performed on Yelp Datasets.…”

Section: Compared Baselinesmentioning

confidence: 99%

GrFrauder: A Novel Unsupervised Clustering Algorithm for Identification Group Spam Reviewers

Chenoori¹,

Kavuri²

2022

ISI

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As e-commerce has expanded, people's lives now include some aspect of online buying, because buyers frequently use online product reviews to make purchasing decisions. Merchants frequently collaborate with review spammers to write spam reviews that promote or demote selected items. Spammers who work in groups, in particular, are more dangerous than individual attacks. Previous studies provided various frequent item mining and graphbased techniques to detect such spammer groups. In this paper, we recommend a technique referred to as GrFrauder (Group Fraud detection) method to detect online spam reviewer groups with an unsupervised manner. Our technology identifies spammer candidate groups initially based on product -product review graph and collaboration among reviewers constructed with several behavioral patterns. It then embeds reviewers into an embedding space and calculates spam score for every group; with higher spam scores the model generates ranks for each group. Studies using four real-world datasets reveal that GrFrauder outperforms numerous state-of-the-art baselines in terms of precision and is able to identify more high-quality spammer groups.

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Spammer group detection and diversification of customers’ reviews

Cited by 7 publications

References 35 publications

Detecting Product Review Spammers Using Principles of Big Data

Detecting Product Review Spammers Using Principles of Big Data

Policy-Based Spam Detection of Tweets Dataset

GrFrauder: A Novel Unsupervised Clustering Algorithm for Identification Group Spam Reviewers

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