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

被引：0

作者：

De Arriba-Perez, Francisco ^{[1
]}

Garcia-Mendez, Silvia ^{[1
]}

Leal, Fatima ^{[2
]}

Malheiro, Benedita ^{[3
,4
]}

Burguillo, Juan C. ^{[1
]}

机构：

[1] Univ Vigo, atlanTTic, Informat Technol Grp, Vigo, Spain

[2] Univ Portucalense, Res Econ Management & Informat Technol, Porto, Portugal

[3] Polytech Porto, ISEP, Rua Dr Antonio Bernardino De Almeida, P-4249015 Porto, Portugal

[4] Inst Syst & Comp Engn Technol & Sci, Porto, Portugal

来源：

INFORMATICA | 2024年 / 35卷 / 03期

关键词：

data drift; interpretability and explainability; Natural Language Processing; online machine learning; spam detection; DATA STREAM; FRAMEWORK; SYSTEM;

D O I：

10.15388/24-INFOR562

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：483 / 507

页数：25

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