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

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,

Tam Metin

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Bibtex	@araştırma makalesi { pap371613, journal = {PressAcademia Procedia}, eissn = {2459-0762}, address = {Siteler Sok. No.12/18 Maltepe, 34843, Istanbul}, publisher = {PressAcademia}, year = {2017}, volume = {5}, number = {1}, pages = {80 - 87}, doi = {10.17261/Pressacademia.2017.575}, title = {USING TEXTUAL FEATURES FOR THE DETECTION OF VANDALISM IN WIKIPEDIA: A COMPARATIVE APPROACH IN LOW-RESOURCE LANGUAGE SECTIONS}, key = {cite}, author = {Susuri, Arsim and Hamiti, Mentor and Dika, Agni} }
APA	Susuri, A. , Hamiti, M. & Dika, A. (2017). USING TEXTUAL FEATURES FOR THE DETECTION OF VANDALISM IN WIKIPEDIA: A COMPARATIVE APPROACH IN LOW-RESOURCE LANGUAGE SECTIONS . PressAcademia Procedia , 5 (1) , 80-87 . DOI: 10.17261/Pressacademia.2017.575
MLA	Susuri, A. , Hamiti, M. , Dika, A. "USING TEXTUAL FEATURES FOR THE DETECTION OF VANDALISM IN WIKIPEDIA: A COMPARATIVE APPROACH IN LOW-RESOURCE LANGUAGE SECTIONS" . PressAcademia Procedia 5 (2017 ): 80-87 <
Chicago	Susuri, A. , Hamiti, M. , Dika, A. "USING TEXTUAL FEATURES FOR THE DETECTION OF VANDALISM IN WIKIPEDIA: A COMPARATIVE APPROACH IN LOW-RESOURCE LANGUAGE SECTIONS". PressAcademia Procedia 5 (2017 ): 80-87
RIS	TY - JOUR T1 - USING TEXTUAL FEATURES FOR THE DETECTION OF VANDALISM IN WIKIPEDIA: A COMPARATIVE APPROACH IN LOW-RESOURCE LANGUAGE SECTIONS AU - ArsimSusuri, MentorHamiti, AgniDika Y1 - 2017 PY - 2017 N1 - doi: 10.17261/Pressacademia.2017.575 DO - 10.17261/Pressacademia.2017.575 T2 - PressAcademia Procedia JF - Journal JO - JOR SP - 80 EP - 87 VL - 5 IS - 1 SN - -2459-0762 M3 - doi: 10.17261/Pressacademia.2017.575 UR - Y2 - 2022 ER -
EndNote	%0 PressAcademia Procedia USING TEXTUAL FEATURES FOR THE DETECTION OF VANDALISM IN WIKIPEDIA: A COMPARATIVE APPROACH IN LOW-RESOURCE LANGUAGE SECTIONS %A Arsim Susuri , Mentor Hamiti , Agni Dika %T USING TEXTUAL FEATURES FOR THE DETECTION OF VANDALISM IN WIKIPEDIA: A COMPARATIVE APPROACH IN LOW-RESOURCE LANGUAGE SECTIONS %D 2017 %J PressAcademia Procedia %P -2459-0762 %V 5 %N 1 %R doi: 10.17261/Pressacademia.2017.575 %U 10.17261/Pressacademia.2017.575
ISNAD	Susuri, Arsim , Hamiti, Mentor , Dika, Agni . "USING TEXTUAL FEATURES FOR THE DETECTION OF VANDALISM IN WIKIPEDIA: A COMPARATIVE APPROACH IN LOW-RESOURCE LANGUAGE SECTIONS". PressAcademia Procedia 5 / 1 (Haziran 2017): 80-87 .
AMA	Susuri A. , Hamiti M. , Dika A. USING TEXTUAL FEATURES FOR THE DETECTION OF VANDALISM IN WIKIPEDIA: A COMPARATIVE APPROACH IN LOW-RESOURCE LANGUAGE SECTIONS. PAP. 2017; 5(1): 80-87.
Vancouver	Susuri A. , Hamiti M. , Dika A. USING TEXTUAL FEATURES FOR THE DETECTION OF VANDALISM IN WIKIPEDIA: A COMPARATIVE APPROACH IN LOW-RESOURCE LANGUAGE SECTIONS. PressAcademia Procedia. 2017; 5(1): 80-87.
IEEE	A. Susuri , M. Hamiti ve A. Dika , "USING TEXTUAL FEATURES FOR THE DETECTION OF VANDALISM IN WIKIPEDIA: A COMPARATIVE APPROACH IN LOW-RESOURCE LANGUAGE SECTIONS", , c. 5, sayı. 1, ss. 80-87, Haz. 2017, doi:10.17261/Pressacademia.2017.575