Bowdoin: Data Driven Socities 2014 - Data, Information, & Privacy...Now 2/5/14

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Data Driven Societies
Digital & Computational Studies
Bowdoin College
February 5, 2014
Professors Gieseking & Gaze

Lecture Slides "Data, Information, & Privacy...Now"

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Bowdoin: Data Driven Socities 2014 - Data, Information, & Privacy...Now 2/5/14

  1. 1. Data Driven Societies: Data, Information, and Privacy...Now Professors Gaze & Gieseking
  2. 2. As We Enter A New Age...(?) ✦ Information society = a society where creation, distribution, use, and manipulation of information heavily influences everyday and structural political, economic, and cultural activities ! ! What, then, in information? And what, pray tell, is data? wikispaces.com
  3. 3. Data (Recap) Pentland: Digital bread crumbs gathered via the Internet can help track, predict, and control for creating better cities and societies ! Berners-Lee: The WWW is a pool of knowledge in the form of data that is constantly reshaping itself ! Marwick: The Internet does not transcend geography but is physically situated in political, cultural, and economic values of place, and the data we see and create online is influenced by these values
  4. 4. DataViz (Recap) Yau: Let the data guide the way you tell a story through a data visualization ✦ “Interpretation of the data changes based on the visual form it takes on.” ! Tufte: Standards exist for clear and efficient data visualization that correctly expresses findings within data ✦ “The point is to get it right, not to win the case, not to sweep under the rug all the assorted puzzles and inconsistencies that frequently occur in collections of data.”
  5. 5. DataViz (Recap) “To put it another way, there is no such thing as raw data. Data are always translated such that they might be presented. The images, lists, graphs, and maps that represent those data are all interpretations. … The phrase ‘data visualization,’ in that sense, is a bit redundant: data are already a visualization.” —Laura Kurgan
  6. 6. What is the distinction between data and information?
  7. 7. Defining Data vs. Information ✦ Data is are pieces of information, i.e. a set of values of qualitative or quantitative variables. ! ! lithosphere.lithium.com ✦ Information is a message that can be conveyed by a sequence of facts, provided or learned.
  8. 8. Cohen: Information-As-_____ ✦ Information-as-freedom: “enable unimpeded, ‘end-toend’ communication and thereby facilitiate the growth of a vibrant, broadly participatory culture” ! ! ✦ Information-as-control: “enable precise, carefully calibrated control of information flows and thereby facilitate the flourishing of vibrant information markets”
  9. 9. Cohen: Why this matters ✦ Legal, technical, and institutional values and regulations shape the flow of information ! ✦ These flows can support or limit the following: ✦ promote free speech ✦ free choice in the markets ✦ shape the subjectivity we can attain ✦ influence the innovation we can produce ✦ reinforce the creation of political and ethical meanings of our everyday lives, at home and abroad
  10. 10. ScraperWiki Support 1. Open ScraperWiki and view your table 2. See how many days of tweets were gathered, i.e., date ended (if ever) - date begun = total number of days 3. Set reminders for yourself to check on your data and repeat the following steps every x number of days: a. Download the table and create backups b. Once you are sure you have backed up this data, delete the original search c. Run the same exact scrape again
  11. 11. Blog Post #3 As we do our best to represent the experiences of others, it is important to reflect on the ways we are constructing knowledge, how the data came to exist, and how much the data can really offer us as researchers. ! To that end, write a three to five paragraph critique of your Twitter dataset to date regarding representation, and include proper citations.
  12. 12. Next Class: Feb. 10 ✦ Today: information society, data, information, Cohen’s information-as-freedom vs. information-as-control ! ✦ Readings: boyd and Crawford, Crawford, Vis ! ✦ Blog: due 2-10, see handout ! ✦ Next class: ✦ privacy and big data ✦ continue working in R

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