Measuring self-focus bias in community-maintained knowledge repositories

Brent Hecht, Darren Gergle

Research output: Chapter in Book/Report/Conference proceedingConference contribution

78 Scopus citations

Abstract

Self-focus is a novel way of understanding a type of bias in community-maintained Web 2.0 graph structures. It goes beyond previous measures of topical coverage bias by encapsulating both node- and edge-hosted biases in a single holistic measure of an entire community-maintained graph. We outline two methods to quantify self-focus, one of which is very computationally inexpensive, and present empirical evidence for the existence of self-focus using a "hyperlingual"approach that examines 15 different language editions of Wikipedia. We suggest applications of our methods and discuss the risks of ignoring self-focus bias in technological applications.

Original languageEnglish (US)
Title of host publicationC and T 2009 - Proceedings of the 4th International Conference on Communities and Technologies
PublisherAssociation for Computing Machinery
Pages11-19
Number of pages9
ISBN (Print)978-1605587134
DOIs
StatePublished - 2009
Event4th International Conference on Communities and Technologies, C and T 2009 - University Park, PA, United States
Duration: Jun 25 2009Jun 27 2009

Publication series

NameC and T 2009 - Proceedings of the 4th International Conference on Communities and Technologies
Volume2009-January

Conference

Conference4th International Conference on Communities and Technologies, C and T 2009
Country/TerritoryUnited States
CityUniversity Park, PA
Period6/25/096/27/09

Keywords

  • Self-focus
  • Web 2.0
  • Wikipedia
  • bias
  • hyperlingual
  • semantic networks
  • topical coverage

ASJC Scopus subject areas

  • Computer Networks and Communications

Fingerprint

Dive into the research topics of 'Measuring self-focus bias in community-maintained knowledge repositories'. Together they form a unique fingerprint.

Cite this