Wikipedia Quality Depends on How Authors Collaborate
"We found that all-round contributors dominated the best-quality entries," said Sudha Ram, professor at the University of Arizona Eller College of Management. (Thomas Veneklasen)
The quality of entries in the world’s largest open-access online encyclopedia depends on how authors collaborate, University of Arizona Professor Sudha Ram finds.
The patterns of collaboration between Wikipedia contributors have a direct effect on the data quality of an article, according to a new paper co-authored by a University of Arizona professor and graduate student.
Sudha Ram, a UA’s Eller College of Management professor, co-authored the article with Jun Liu, a graduate student in the management information systems department (MIS). Their work in this area received a “Best Paper Award” at the Workshop on Information Technology and Systems held in conjunction with the International Conference on Information Systems, or ICIS.
“Most of the existing research on Wikipedia is at the aggregate level, looking at total number of edits for an article, for example, or how many unique contributors participated in its creation,” said Ram, who is a McClelland Professor of MIS in the Eller College.
“What was missing was an explanation for why some articles are of high quality and others are not,” she said. “We investigated the relationship between collaboration and data quality.”
Wikipedia has an internal quality rating system for entries, with featured articles at the top, followed by A, B, and C-level entries. Ram and Liu randomly collected 400 articles at each quality level and applied a data provenance model they developed in an earlier paper.
“We used data mining techniques and identified various patterns of collaboration based on the provenance or, more specifically, who does what to Wikipedia articles,” Ram says. “These collaboration patterns either help increase quality or are detrimental to data quality.”
Ram and Liu identified seven specific roles that Wikipedia contributors play. (more…)
New Tool for Mathematics Research
Mathematics is driven by the quest to solve problems and today the American Institute of Mathematics (AIM) announces a new tool to help attack those questions. Research problems can take decades or centuries to answer, with partial solutions spawning new problems along the way. Keeping track of all the problems is difficult, even for experts. Sometimes the solution needs an idea from another field, and it can take a long time for someone to notice the connection.
To help address these challenges AIM has developed the AIM Problem Lists. “Old problems need new ideas and the AIM problem lists open up the world of mathematics to a broader audience,” said AIM director Brian Conrey. The problem lists will provide clear statements of problems in the context of related research problems along with expert commentary on possible approaches to a solution. Each problem list provides a snapshot of a specialized area of research.
All versions of the problem lists will be permanently archived through the Harvard IQSS Dataverse Network. “The record of changes to a problem list will provide a moving picture of progress in mathematics research,” said Micah Altman, Senior Research Scientist at Harvard’s Institute for Quantitative Social Science. These records will allow historians to track developments in a way that previously has not been possible. (more…)
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