Measuring self-focus bias in community-maintained knowledge repositories
| dc.contributor.author | Hecht, Brent | |
| dc.contributor.author | Gergle, Darren | |
| dc.date.accessioned | 2017-04-15T12:04:04Z | |
| dc.date.available | 2017-04-15T12:04:04Z | |
| dc.date.issued | 2009 | |
| dc.description.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. | |
| dc.identifier.doi | 10.1145/1556460.1556463 | |
| dc.language.iso | en | |
| dc.publisher | ACM Press | |
| dc.relation.ispartof | Communities and Technologies 2009: Proceedings of the Fourth Communities and Technologies Conference | |
| dc.relation.ispartofseries | Communities and Technologies | |
| dc.title | Measuring self-focus bias in community-maintained knowledge repositories | |
| dc.type | Text | |
| gi.citation.endPage | 20 | |
| gi.citation.startPage | 11 | |
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| gi.conference.sessiontitle | Full Papers |