Experiments in Social Data Mining

Brian Amento, Will Hill, Deborah Hix, Robert Schulman, Loren Terveen

Research output: Contribution to journalArticlepeer-review

22 Scopus citations


Social data mining systems enable people to share opinions and benefit from each other's experience. They do this by mining and redistributing information from computational records of social activity such as Usenet messages, system usage history, citations, or hyperlinks. Some general questions for evaluating such systems are: (1) is the extracted information valuable? and (2) do interfaces based on the information improve user task performance? We report here on TopicShop, a system that mines information from the structure and content of Web pages and provides an exploratory information workspace interface. We carried out experiments that yielded positive answers to both evaluation questions. First, a number of automatically computable features about Web sites do a good job of predicting expert quality judgments about sites. Second, compared to popular Web search interfaces, the TopicShop interface to this information lets users select significantly more high-quality sites, in less time and with less effort, and to organize the sites they select into personally meaningful collections more quickly and easily. We conclude by discussing how our results may be applied and considering how they touch on general issues concerning quality, expertise, and consensus.

Original languageEnglish (US)
Pages (from-to)54-85
Number of pages32
JournalACM Transactions on Computer-Human Interaction
Issue number1
StatePublished - Mar 1 2003


  • Cocitation analysis
  • Experimentation
  • Human Factors
  • collaborative filtering
  • computer-supported cooperative work
  • information visualization
  • social filtering
  • social network analysis


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