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Bowdoin: Data Driven Socities 2014 - Defining Data & Redefining Privacy 2/10/14
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Bowdoin: Data Driven Socities 2014 - Defining Data & Redefining Privacy 2/10/14

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Data Driven Societies …

Data Driven Societies
Digital & Computational Studies
Bowdoin College
February 10, 2014
Professors Gieseking & Gaze

Lecture Slides "Defining Data & Redefining Privacy"


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  • 1. Data Driven Societies: Defining Big Data & Redefining Privacy Professors Gaze & Gieseking
  • 2. Data & Information (Recap) ✦ Information society ! ✦ Data vs. information ! ✦ Information-as-freedom vs. information-as-control
  • 3. Big Data & Privacy ✦ Ethical research ! ✦ Data sample and data access ! ✦ Defining big data, defining privacy daily.captaindash.com
  • 4. Social Scientific Approach 0. Identify an issue 1. Research question 2. Theoretical approach 3. Literature review 4. Methods 5. Analysis 6. Discussion 7. Conclusion
  • 5. Social Scientific Approach 0. Identify an issue 1. Research question 2. Theoretical approach 3. Literature review 4. Methods 5. Analysis 6. Discussion 7. Conclusion
  • 6. Social Scientific Approach 0. Identify an issue 1. Research question 2. Theoretical approach 3. Literature review 4. Methods 5. Analysis 6. Discussion 7. Conclusion
  • 7. Social Scientific Approach 0. Identify an issue 1. Research question 2. Theoretical approach 3. Literature review 4. Methods 5. Analysis 6. Discussion 7. Conclusion Ethics, anyone?
  • 8. The Future of Now The Chronicle of Higher Ed The White House
  • 9. Visualize This How we handle to emergence of Big Data is critical. …it is still necessary to ask critical questions about what all this data means, who gets access to what data, how data analysis is deployed, and to what ends. —danah boyd & Kate Crawford, “Critical Questions for Big Data” (2012)
  • 10. Research Ethics ✦ Information Review Board (IRB) ✦ Informed consent ✦ Risk ✦ Accountability !
  • 11. Sample
  • 12. Sampling & Access http://blog.globalwebindex.net/
  • 13. Data Access: Twitter ✦ API - application programming interface is the set of tools developers can use to access structured data ! ✦ “Firehose” of access: GNIP, DataSifter ✦ “Gardenhose" of access: 10% of public tweets ✦ “Spritzer” of access: about 1% of public tweets ✦ White-listed accounts: allowed access to certain subject matter
  • 14. Data Rich and the Data Poor Manovich (2011) writes of three classes of people in the realm of Big Data: “those who create data (both consciously and by leaving digital footprints), those who have the means to collect it, and those have expertise to analyze it.” -boyd & Crawford (2012) ! ✦ Data rich and data poor - research insiders and outsiders, respectively, who have varied degrees of access to data and the means to analyze it
  • 15. Defining Big Data 1. Large data sets that require supercomputers for analysis, i.e., usually over 2gb (Manovich 2011) ! 2. A cultural, technological, and scholarly phenomenon that depends on the interplay of the following: ✦ Technology: maximized computation power and algorithmic accuracy ✦ Analysis: examining large data sets to identify patterns to make claims ✦ Mythology: widespread brief that the larger the data set, the more accurate the findings (boyd & Crawford 2012)
  • 16. Defining Privacy To be continued…
  • 17. ScraperWiki Support A clever and elegant solution to our problem of accessing Twitter data with a limited number of calls: ! 1. Open ScraperWiki and view your table ! 2. Download EVERY MONDAY ! 3. Restart EVERY MONDAY (you will need to do this the first Monday of break too)
  • 18. Next Class: Feb. 12 Today: big data, privacy, research ethics, data rich vs. data poor ✦ ! Quiz: terms / concepts coming via email ✦ ! Readings: Pariser, Stray ✦ ! ✦ Next class/lab: ✦ filter bubbles ✦ correlation/causation ✦ work with Twitter datasets ✦ continue learning R

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