Detecting Spam Game Reviews on Steam with a Semi-Supervised Approach

Bian, Pengze; Liu, Lei; Sweetser, Penny

doi:10.1145/3472538.3472547

Cited by 3 publications

(2 citation statements)

References 39 publications

(61 reference statements)

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“…As previously mentioned, online reviews have become an essential source of information for consumers to make purchasing decisions (Zhang et al, 2018;Al-Otaibi and Al-Rasheed, 2022). However, spam reviews, which are fake or biased reviews, have become a significant problem, leading to distrust and confusion among consumers (Bian et al, 2021). 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).…”

Section: Related Workmentioning

confidence: 99%

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%

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

“…This makes SAR the most generalist approach, as it acknowledges the influence of inherent biases in various real-world applications [Bekker and Davis 2018]. For instance, in tasks such as spam detection in emails or product review analysis, the selection of positive examples to label may depend on the compelling nature of the text itself [Bian et al 2021, Wu et al 2020. Similarly, recommendation systems might be influenced by the order in which the initial products or services are presented, which can bias subsequent recommendations.…”

Section: Labeling Mechanismmentioning

confidence: 99%

Positive Unlabeled Learning: Adapting NMF for text classification

Nunes,

Faleiros,

Rossi

2023

Anais Do XX Encontro Nacional De Inteligência Artificial E Computacional (ENIAC 2023)

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Due to the overwhelming data generation that surpasses human evaluation capacity, manually labeling data for training machine learning models is becoming increasingly impractical. This article focuses on analyzing techniques to address the challenges of Positive Unlabeled Learning (PUL). To this end, we propose structural adaptations to the Non-Negative Matrix Factorization (NMF) algorithm, specifically tailored for PU data (NMFPUL). We compare NMFPUL with state-of-the-art techniques to identify improvements in the performance of textual data classification. Our study reveals that NMFPUL consistently outperforms most baseline algorithms across diverse document collections even with a limited number of labeled documents, and mainly on these situations.

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Spam Reviews Detection in the Time of COVID-19 Pandemic: Background, Definitions, Methods and Literature Analysis

2022

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During the recent COVID-19 pandemic, people were forced to stay at home to protect their own and others’ lives. As a result, remote technology is being considered more in all aspects of life. One important example of this is online reviews, where the number of reviews increased promptly in the last two years according to Statista and Rize reports. People started to depend more on these reviews as a result of the mandatory physical distance employed in all countries. With no one speaking to about products and services feedback. Reading and posting online reviews becomes an important part of discussion and decision-making, especially for individuals and organizations. However, the growth of online reviews usage also provoked an increase in spam reviews. Spam reviews can be identified as fraud, malicious and fake reviews written for the purpose of profit or publicity. A number of spam detection methods have been proposed to solve this problem. As part of this study, we outline the concepts and detection methods of spam reviews, along with their implications in the environment of online reviews. The study addresses all the spam reviews detection studies for the years 2020 and 2021. In other words, we analyze and examine all works presented during the COVID-19 situation. Then, highlight the differences between the works before and after the pandemic in terms of reviews behavior and research findings. Furthermore, nine different detection approaches have been classified in order to investigate their specific advantages, limitations, and ways to improve their performance. Additionally, a literature analysis, discussion, and future directions were also presented.

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Detecting Spam Game Reviews on Steam with a Semi-Supervised Approach

Cited by 3 publications

References 39 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

Positive Unlabeled Learning: Adapting NMF for text classification

Spam Reviews Detection in the Time of COVID-19 Pandemic: Background, Definitions, Methods and Literature Analysis

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