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Simulation, modelling and classification of wiki contributors: Spotting the good, the bad, and the ugly

García-Méndez, Silvia, Leal, Fátima, Malheiro, Benedita, Burguillo-Rial, Juan Carlos, Veloso, Bruno, Chis, Adriana E. and González-Vélez, Horacio (2022) Simulation, modelling and classification of wiki contributors: Spotting the good, the bad, and the ugly. Simulation Modelling Practice and Theory, 120. p. 102616. ISSN 1569-190X

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Official URL: https://doi.org/10.1016/j.simpat.2022.102616

Abstract

Data crowdsourcing is a data acquisition process where groups of voluntary contributors feed platforms with highly relevant data ranging from news, comments, and media to knowledge and classifications. It typically processes user-generated data streams to provide and refine popular services such as wikis, collaborative maps, e-commerce sites, and social networks. Nevertheless, this modus operandi raises severe concerns regarding ill-intentioned data manipulation in adversarial environments. This paper presents a simulation, modelling, and classification approach to automatically identify human and non-human (bots) as well as benign and malign contributors by using data fabrication to balance classes within experimental data sets, data stream modelling to build and update contributor profiles and, finally, autonomic data stream classification. By employing WikiVoyage – a free worldwide wiki travel guide open to contribution from the general public – as a testbed, our approach proves to significantly boost the confidence and quality of the classifier by using a class-balanced data stream, comprising both real and synthetic data. Our empirical results show that the proposed method distinguishes between benign and malign bots as well as human contributors with a classification accuracy of up to 92 %.

Item Type: Article
Uncontrolled Keywords: Classification; Data reliability; Stream processing; Synthetic data; Data fabrication; Wiki contributors
Subjects: Q Science > QA Mathematics > Electronic computers. Computer science
T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science
Divisions: School of Computing > Staff Research and Publications
Related URLs:
Depositing User: Clara Chan
Date Deposited: 27 Jun 2022 15:10
Last Modified: 27 Jun 2022 15:10
URI: https://norma.ncirl.ie/id/eprint/5618

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