Editing Behavior Analysis for Predicting Active and Inactive Users in Wikipedia

Harish Arelli; Francesca Spezzano; Anu Shrestha

Editing Behavior Analysis for Predicting Active and Inactive Users in Wikipedia

Harish Arelli, Francesca Spezzano, Anu Shrestha

Computer Science Department

Boise State University

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

Abstract

These days, user-generated content platforms such as social media, question-answering Websites, and open collaboration systems are a source of information for many. These platforms survive, thanks to the pool of active contributors who generate content. As a consequence, they continuously face the problem of acquiring new users and retain them in the platform. In this paper, we study the case of English Wikipedia, a well-established open collaboration system, and study the problem of predicting whether or not an editor will become inactive and stop contributing to the encyclopedia. Knowing this information can help the administrative community to perform engaging actions on time to keep users contributing longer. We propose a predictive model leveraging contributors’ editing behavior to identify active vs. inactive Wikipedia users. Our experiments show that our method achieves an AUROC of at least 0.97 in predicting editors who will become inactive and can predict inactive users earlier than the baseline.

Original language	American English
Title of host publication	Influence and Behavior Analysis in Social Networks and Social Media
State	Published - 1 Jan 2019

EGS Disciplines

Computer Sciences

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https://dx.doi.org/10.1007/978-3-030-02592-2_7

Cite this

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title = "Editing Behavior Analysis for Predicting Active and Inactive Users in Wikipedia",

abstract = " These days, user-generated content platforms such as social media, question-answering Websites, and open collaboration systems are a source of information for many. These platforms survive, thanks to the pool of active contributors who generate content. As a consequence, they continuously face the problem of acquiring new users and retain them in the platform. In this paper, we study the case of English Wikipedia, a well-established open collaboration system, and study the problem of predicting whether or not an editor will become inactive and stop contributing to the encyclopedia. Knowing this information can help the administrative community to perform engaging actions on time to keep users contributing longer. We propose a predictive model leveraging contributors’ editing behavior to identify active vs. inactive Wikipedia users. Our experiments show that our method achieves an AUROC of at least 0.97 in predicting editors who will become inactive and can predict inactive users earlier than the baseline. ",

author = "Harish Arelli and Francesca Spezzano and Anu Shrestha",

year = "2019",

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day = "1",

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TY - CHAP

T1 - Editing Behavior Analysis for Predicting Active and Inactive Users in Wikipedia

AU - Arelli, Harish

AU - Spezzano, Francesca

AU - Shrestha, Anu

PY - 2019/1/1

Y1 - 2019/1/1

N2 - These days, user-generated content platforms such as social media, question-answering Websites, and open collaboration systems are a source of information for many. These platforms survive, thanks to the pool of active contributors who generate content. As a consequence, they continuously face the problem of acquiring new users and retain them in the platform. In this paper, we study the case of English Wikipedia, a well-established open collaboration system, and study the problem of predicting whether or not an editor will become inactive and stop contributing to the encyclopedia. Knowing this information can help the administrative community to perform engaging actions on time to keep users contributing longer. We propose a predictive model leveraging contributors’ editing behavior to identify active vs. inactive Wikipedia users. Our experiments show that our method achieves an AUROC of at least 0.97 in predicting editors who will become inactive and can predict inactive users earlier than the baseline.

AB - These days, user-generated content platforms such as social media, question-answering Websites, and open collaboration systems are a source of information for many. These platforms survive, thanks to the pool of active contributors who generate content. As a consequence, they continuously face the problem of acquiring new users and retain them in the platform. In this paper, we study the case of English Wikipedia, a well-established open collaboration system, and study the problem of predicting whether or not an editor will become inactive and stop contributing to the encyclopedia. Knowing this information can help the administrative community to perform engaging actions on time to keep users contributing longer. We propose a predictive model leveraging contributors’ editing behavior to identify active vs. inactive Wikipedia users. Our experiments show that our method achieves an AUROC of at least 0.97 in predicting editors who will become inactive and can predict inactive users earlier than the baseline.

UR - https://scholarworks.boisestate.edu/cs_facpubs/249

UR - https://dx.doi.org/10.1007/978-3-030-02592-2_7

M3 - Chapter

BT - Influence and Behavior Analysis in Social Networks and Social Media

ER -

Editing Behavior Analysis for Predicting Active and Inactive Users in Wikipedia

Abstract

EGS Disciplines

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