Who Will Stop Contributing?: Predicting Inactive Editors in Wikipedia

Harish Arelli; Francesca Spezzano

doi:10.1145/3110025.3110026

Who Will Stop Contributing?: Predicting Inactive Editors in Wikipedia

Harish Arelli, Francesca Spezzano

Computer Science Department

Boise State University

Research output: Chapter in Book/Report/Conference proceeding › Chapter

2 Scopus citations

Abstract

In this paper, we focus on English Wikipedia, one of the main user-contributed content systems, and study the problem of predicting which users will become inactive and stop contributing to the encyclopedia. We propose a predictive model leveraging frequent patterns appearing in user's editing behavior as features to predict active vs. inactive Wikipedia users. Our experiments show that our method can effectively predict inactive users with an AUROC of 0.97 and significantly beats competitors in the task of early prediction of inactive users.

Original language	American English
Title of host publication	ASONAM '17: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017
Editors	Jana Diesner, Elena Ferrari, Guandong Xu
Pages	355-358
Number of pages	4
ISBN (Electronic)	9781450349932
DOIs	https://doi.org/10.1145/3110025.3110026
State	Published - 1 Jan 2017
Event	9th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017 - Sydney, Australia Duration: 31 Jul 2017 → 3 Aug 2017

Publication series

Name	Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017

Conference

Conference	9th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017
Country/Territory	Australia
City	Sydney
Period	31/07/17 → 3/08/17

EGS Disciplines

Computer Sciences

Access to Document

10.1145/3110025.3110026License: Unspecified

https://doi.org/10.1145/3110025.3110026

Cite this

Arelli, H., & Spezzano, F. (2017). Who Will Stop Contributing?: Predicting Inactive Editors in Wikipedia. In J. Diesner, E. Ferrari, & G. Xu (Eds.), ASONAM '17: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017 (pp. 355-358). (Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017). https://doi.org/10.1145/3110025.3110026

Arelli, Harish ; Spezzano, Francesca. / Who Will Stop Contributing?: Predicting Inactive Editors in Wikipedia. ASONAM '17: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017. editor / Jana Diesner ; Elena Ferrari ; Guandong Xu. 2017. pp. 355-358 (Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017).

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title = "Who Will Stop Contributing?: Predicting Inactive Editors in Wikipedia",

abstract = " In this paper, we focus on English Wikipedia, one of the main user-contributed content systems, and study the problem of predicting which users will become inactive and stop contributing to the encyclopedia. We propose a predictive model leveraging frequent patterns appearing in user's editing behavior as features to predict active vs. inactive Wikipedia users. Our experiments show that our method can effectively predict inactive users with an AUROC of 0.97 and significantly beats competitors in the task of early prediction of inactive users.",

author = "Harish Arelli and Francesca Spezzano",

note = "Publisher Copyright: {\textcopyright} 2017 Association for Computing Machinery.; 9th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017 ; Conference date: 31-07-2017 Through 03-08-2017",

year = "2017",

month = jan,

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doi = "10.1145/3110025.3110026",

language = "American English",

series = "Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017",

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editor = "Jana Diesner and Elena Ferrari and Guandong Xu",

booktitle = "ASONAM '17: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017",

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Arelli, H & Spezzano, F 2017, Who Will Stop Contributing?: Predicting Inactive Editors in Wikipedia. in J Diesner, E Ferrari & G Xu (eds), ASONAM '17: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017. Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017, pp. 355-358, 9th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017, Sydney, Australia, 31/07/17. https://doi.org/10.1145/3110025.3110026

Who Will Stop Contributing?: Predicting Inactive Editors in Wikipedia. / Arelli, Harish; Spezzano, Francesca.
ASONAM '17: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017. ed. / Jana Diesner; Elena Ferrari; Guandong Xu. 2017. p. 355-358 (Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017).

Research output: Chapter in Book/Report/Conference proceeding › Chapter

TY - CHAP

T1 - Who Will Stop Contributing?: Predicting Inactive Editors in Wikipedia

AU - Arelli, Harish

AU - Spezzano, Francesca

PY - 2017/1/1

Y1 - 2017/1/1

N2 - In this paper, we focus on English Wikipedia, one of the main user-contributed content systems, and study the problem of predicting which users will become inactive and stop contributing to the encyclopedia. We propose a predictive model leveraging frequent patterns appearing in user's editing behavior as features to predict active vs. inactive Wikipedia users. Our experiments show that our method can effectively predict inactive users with an AUROC of 0.97 and significantly beats competitors in the task of early prediction of inactive users.

AB - In this paper, we focus on English Wikipedia, one of the main user-contributed content systems, and study the problem of predicting which users will become inactive and stop contributing to the encyclopedia. We propose a predictive model leveraging frequent patterns appearing in user's editing behavior as features to predict active vs. inactive Wikipedia users. Our experiments show that our method can effectively predict inactive users with an AUROC of 0.97 and significantly beats competitors in the task of early prediction of inactive users.

UR - https://scholarworks.boisestate.edu/cs_facpubs/136

UR - https://doi.org/10.1145/3110025.3110026

UR - https://www.scopus.com/pages/publications/85040224986

U2 - 10.1145/3110025.3110026

DO - 10.1145/3110025.3110026

M3 - Chapter

T3 - Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017

SP - 355

EP - 358

BT - ASONAM '17: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017

A2 - Diesner, Jana

A2 - Ferrari, Elena

A2 - Xu, Guandong

T2 - 9th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017

Y2 - 31 July 2017 through 3 August 2017

ER -

Arelli H, Spezzano F. Who Will Stop Contributing?: Predicting Inactive Editors in Wikipedia. In Diesner J, Ferrari E, Xu G, editors, ASONAM '17: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017. 2017. p. 355-358. (Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017). doi: 10.1145/3110025.3110026

Who Will Stop Contributing?: Predicting Inactive Editors in Wikipedia

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