Zimmer CSCW 2010
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Zimmer CSCW 2010

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Brief presentation on ethical issues related to archiving and releasing research data from social networking sites.

Brief presentation on ethical issues related to archiving and releasing research data from social networking sites.

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    Zimmer CSCW 2010 Zimmer CSCW 2010 Presentation Transcript

    • Facebook Research Ethics
      • What data is in your database?
      • How did you gather it?
      • How is the data being archived?
      Archiving Data “ Tastes, Ties, and Time” research project Case
      • Why are you releasing the data?
      • To whom are you releasing the data?
      • How is the data being released?
      Releasing Data Michael Zimmer  School of Information Studies  UW-Milwaukee
      • “ If it’s on Facebook, there’s no privacy”
      • “ We’ve anonymized the dataset”
      Fallacies
    • Archiving Data
      • Do you have just basic demographic information? (gender, # of friends, last active)
      • Or, do you have sensitive / identifiable information (name, hometown, favorites)
      What’s in your database?
      • Did you obtain informed consent from Facebook user to collect her data?
      • Did you fool user into friending, or use an application to gain access?
      • Did you use a bot to scrape profiles?
      How did you gather it?
      • Has it been “ de-identified ”?
      • Is it encrypted ? Restricted access?
      How is it archived?
    • Releasing Data
      • Are you mandated to release the data?
      • Do you feel all data wants to be free ?
      Why?
      • Is the data publicly available to anyone?
      • Are you screening requests to gain access?
      To whom?
      • Is all data being released?
      • Has it been scrubbed or “anonymized”?
      • Will you monitor use of the data?
      How?
    • Fallacies
      • “ If you put it on Facebook, you have no expectation of privacy”
      • False dichotomy of public/private information
      • Privacy is contextual (Nissenbaum)
      • Just because posted on Facebook doesn’t mean consented to have it scraped, stored, used, and distributed by anyone for anything
      Privacy
      • “ We’ve anonymized our dataset”
      • Attempts to anonymize regularly fail (Ohm)
      • Any piece of information could be potentially linkable to reveal identity
      • Need to rethink anonymization as sufficient
      Anonymization