USING TEXTUAL FEATURES FOR THE DETECTION OF VANDALISM IN WIKIPEDIA: A COMPARATIVE APPROACH IN LOW-RESOURCE LANGUAGE SECTIONS

Year 2017, Volume: 5 Issue: 1, 80 - 87, 30.06.2017

Arsim Susuri Mentor Hamiti Agni Dika

https://doi.org/10.17261/Pressacademia.2017.575

Abstract

This study
investigates the impact of using textual features for the detection of
vandalism across low-resource language sections in Wikipedia. For this purpose,
we propose new features that allow the machine learning-based text classifiers
to better distinguish vandalism and to improve the detection rates of vandalism
across languages, based on textual features applied in previous researches.
These features enable us to compare the contributions of the bots against
vandalism, stressing the differences between bots and editors with regards to
the detection of vandalism. We propose a new set of efficient and language
independent features, which has the performance level similar to the previous
sets. Three Wikipedia sections will be used for this purpose: Simple English
(simple), Albanian (sq) and Bosnian (bs). We will show that our set of textual
features has similar and, in some cases, better vandalism detection rates
across languages than previous research. 

 

Keywords

Wikipedia, textual features, low-resource languages, vandalism

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