Implementation and Evaluation of a Framework to calculate Impact Measures for Wikipedia Authors

TL;DR

This paper presents an open-source distributed framework using MapReduce to efficiently calculate impact measures for Wikipedia authors, aiding in identifying influential or harmful contributors.

Contribution

It introduces a scalable, extensible framework for impact measure calculation and evaluates performance optimizations on large datasets.

Findings

Horizontal scaling reduces processing time

Framework effectively handles large datasets

Reimplementation demonstrates extensibility and usability

Abstract

Wikipedia, an open collaborative website, can be edited by anyone, even anonymously, thus becoming victim to ill-intentioned changes. Therefore, ranking Wikipedia authors by calculating impact measures based on the edit history can help to identify reputational users or harmful activity such as vandalism \cite{Adler:2008:MAC:1822258.1822279}. However, processing millions of edits on one system can take a long time. The author implements an open source framework to calculate such rankings in a distributed way (MapReduce) and evaluates its performance on various sized datasets. A reimplementation of the contribution measures by \citeauthor{Adler:2008:MAC:1822258.1822279} demonstrates its extensibility and usability, as well as problems of handling huge datasets and their possible resolutions. The results put different performance optimizations into perspective and show that horizontal scaling can decrease the total processing time.