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

共 50 条

[1] Opinion Spam Detection in Online Reviews Using Neural Networks
Archchitha, K.
Charles, E. Y. A.
2019 19TH INTERNATIONAL CONFERENCE ON ADVANCES IN ICT FOR EMERGING REGIONS (ICTER - 2019), 2019,
[2] Towards Reliable Drift Detection and Explanation in Text Data
Feldhans, Robert
Hammer, Barbara
INTELLIGENT DATA ENGINEERING AND AUTOMATED LEARNING - IDEAL 2024, PT I, 2025, 15346 : 301 - 312
[3] NetSpam: A Network-Based Spam Detection Framework for Reviews in Online Social Media
Shehnepoor, Saeedreza
Salehi, Mostafa
Farahbakhsh, Reza
Crespi, Noel
IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, 2017, 12 (07) : 1585 - 1595
[4] Detection of spam reviews: a sentiment analysis approach
Sunil Saumya
Jyoti Prakash Singh
CSI Transactions on ICT, 2018, 6 (2) : 137 - 148
[5] An Investigation of Ensemble Techniques for Detection of Spam Reviews
Heredia, Brian
Khoshgoftaar, Taghi M.
Prusa, Joseph
Crawford, Michael
2016 15TH IEEE INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS (ICMLA 2016), 2016, : 127 - 133
[6] Exploiting the Spam Correlations in Scalable Online Social Spam Detection
Xu, Hailu
Hu, Liting
Liu, Pinchao
Guan, Boyuan
CLOUD COMPUTING - CLOUD 2019, 2019, 11513 : 146 - 160
[7] Harnessing the Nature of Spam in Scalable Online Social Spam Detection
Xu, Hailu
Guan, Boyuan
Liu, Pinchao
Escudero, William
Hu, Liting
2018 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA), 2018, : 3733 - 3736
[8] Rival Learner Algorithm with Drift Adaptation for Online Data Stream Regression
Liao, Zhenwei
Wang, Yongheng
2018 INTERNATIONAL CONFERENCE ON ALGORITHMS, COMPUTING AND ARTIFICIAL INTELLIGENCE (ACAI 2018), 2018,
[9] Towards Online Review Spam Detection
Lin, Yuming
Zhu, Tao
Wang, Xiaoling
Zhang, Jingwei
Zhou, Aoying
WWW'14 COMPANION: PROCEEDINGS OF THE 23RD INTERNATIONAL CONFERENCE ON WORLD WIDE WEB, 2014, : 341 - 342
[10] AI tools for debunking online spam reviews? Trust of younger and older adults in AI detection criteria
Xiang, Honglian
Zhou, Jia
Xie, Bingjun
BEHAVIOUR & INFORMATION TECHNOLOGY, 2023, 42 (05) : 478 - 497

← 1 2 3 4 5 →