Measuring self-focus bias in community-maintained knowledge repositories

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dc.contributor.authorHecht, Brent
dc.contributor.authorGergle, Darren
dc.date.accessioned2017-04-15T12:04:04Z
dc.date.available2017-04-15T12:04:04Z
dc.date.issued2009
dc.description.abstractSelf-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.
dc.identifier.doi10.1145/1556460.1556463
dc.language.isoen
dc.publisherACM Press
dc.relation.ispartofCommunities and Technologies 2009: Proceedings of the Fourth Communities and Technologies Conference
dc.relation.ispartofseriesCommunities and Technologies
dc.titleMeasuring self-focus bias in community-maintained knowledge repositories
dc.typeText
gi.citation.endPage20
gi.citation.startPage11
gi.conference.sessiontitleFull Papers

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C&T 2009: Proceedings of the Fourth Communities and Technologies Conference