Insights from the Wikipedia Contest (IEEE Contest for Data Mining 2011)

TL;DR

This paper discusses the development of predictive models for future Wikipedia editing activity based on editing history, aiming to understand factors influencing editor retention and engagement.

Contribution

It presents a modeling approach and insights from a data mining contest focused on predicting editor activity, highlighting key predictors of future engagement.

Findings

Identified key factors influencing editor retention

Developed models with predictive accuracy for future edits

Gained insights into editor engagement dynamics

Abstract

The Wikimedia Foundation has recently observed that newly joining editors on Wikipedia are increasingly failing to integrate into the Wikipedia editors' community, i.e. the community is becoming increasingly harder to penetrate. To sustain healthy growth of the community, the Wikimedia Foundation aims to quantitatively understand the factors that determine the editing behavior, and explain why most new editors become inactive soon after joining. As a step towards this broader goal, the Wikimedia foundation sponsored the ICDM (IEEE International Conference for Data Mining) contest for the year 2011. The objective for the participants was to develop models to predict the number of edits that an editor will make in future five months based on the editing history of the editor. Here we describe the approach we followed for developing predictive models towards this goal, the results that we obtained and the modeling insights that we gained from this exercise. In addition, towards the broader goal of Wikimedia Foundation, we also summarize the factors that emerged during our model building exercise as powerful predictors of future editing activity.