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

IF 3.3 4区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Informatica Pub Date : 2024-06-17 DOI:10.15388/24-infor562

Francisco de Arriba-Pérez, Silvia García-Méndez, Fátima Leal, Benedita Malheiro, Juan C. Burguillo

引用次数: 0

Abstract

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. PDF XML

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利用数据漂移自适应技术在线检测垃圾评论并进行信息图表说明

垃圾评论是网络平台上一个普遍存在的问题，因为它会对声誉产生重大影响。然而，有关数据流中垃圾评论检测的研究却很少。另一个令人担忧的问题是垃圾评论需要透明。因此，本文针对这些问题提出了一种在线解决方案，用于识别和解释垃圾评论，并结合数据漂移适应。它整合了（i）增量剖析，（ii）数据漂移检测& 适应，以及（iii）利用机器学习识别垃圾评论。可解释机制在仪表板中显示可视化和文本预测解释。获得的最佳结果是，垃圾邮件 F-measure 高达 87%。PDF XML

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Informatica 工程技术-计算机：信息系统

CiteScore

5.90

自引率

6.90%

发文量

审稿时长

12 months

期刊介绍： The quarterly journal Informatica provides an international forum for high-quality original research and publishes papers on mathematical simulation and optimization, recognition and control, programming theory and systems, automation systems and elements. Informatica provides a multidisciplinary forum for scientists and engineers involved in research and design including experts who implement and manage information systems applications.