This semester I am teaching Data Driven Societies as part of the new Digital and Computatial Studies Initiative at Bowdoin College. My co-instructor is the awesome Eric Gaze, Director of the Quantitative Reasoning Program and President of the National Numeracy Network. With 35 very excited students, we are where the social data sciences meet.
In the first week of class, students will select a hashtag on a social issue of their choice, and scraping their hashtag data off of Twitter for at least a month. In labs, student will apply graph (Excel, R), spatial (GoogleMaps, Social Explorer), and network analysis (Gephi) skills learned in class to create data visualizations from their dataset and interpret the data. The readings we read in class will help them deal with some of the major issues framing issues of the web today, including Defining Data, Private(s), Public(s), The Life of Code, Sociality and Cyborgembodiment, and Power and Format. Quizzes will focus on key concepts learned in readings and labs, and the summary exam will include material from quizzes. The class concludes with students drafting a final paper from their blog posts and visuals into a summary analysis of the trends they see in their data.
As always, I welcome feedback and I’m eager to share with folks on similar paths. Data and society, onward!
Interdisciplinary Studies 2420
Data Driven Societies || Spring 2014
Professor Eric Gaze & Professor Jack Jen Gieseking
Interpretation of the data changes based on the visual form it takes on. – Nathan Yau
Big data and computational methods, such as changes in social media privacy laws and advances in mapping and network analysis, are changing financial markets, political campaigning, and higher education and becoming commonplace in our lives. Our daily existence is increasingly structured by code, from the algorithms that time our traffic lights to those that filter our search criteria and record our thoughts and ideas. In this course, we explore the possibilities, limitations, and implications of using digital and computational methods and analytics to study issues that affect our everyday lives from a social scientific approach. We pay special attention to the ways we collect, trust, analyze, portray, and use data, most especially the tools and meanings involved in data visualization and modeling.
This course tackles a number of cutting-edge issues and questions that confront society today: What sorts of questions can be asked and answered using digital and computational methods to rethink our relationships to data and what can data can show us about the world? How do we construct models to help us better understand social phenomena and associated data? What is data, and how do we know it’s reliable? How do these methods complement and sometimes challenge traditional methodologies in the social sciences? Students will leave the course with both substantive experience in digital and computational methods, Students will learn how to apply a critical lens for understanding and evaluating what computers can (and cannot) bring to the study of society.
This class is a project-based, collaborative learning experience and experiment. For most of the semester there will be regular class readings for which you are responsible and a number of short assignments, including blog posts and comments, in-class quizzes, lab projects, and a hackathon. This will prepare you for the capstones of the course: an independent research paper and presentation, and an exam. Throughout the semester, time will be devoted to developing and implementing these projects in a series of scaffolded posts and quizzes as a process for you to set criteria for, build, and then evaluate your own project’s impact. Note: all blogs posts are due by 8 p.m. the day before class. Quiz and blog post/comment topics will be clarified in advance during class.
Blog posts and comments 15%
Paper & presentation 35%
Website, Textbooks, Texts, and Other Sources
You are required to acquire or make use of the following textbooks from ILL. The remainder of the texts are on e-reserve or can be found online.
- Course website: https://courses.bowdoin.edu/interdisciplinary-studies-2420-spring-2014/
- Course eReserves: http://ereserves.bowdoin.edu/
- Golbeck, Jennifer. 2013. Analyzing the Social Web. Waltham, MA: Morgan Kaufmann/Elsevier.
- Yau, Nathan. 2011. Visualize This: The FlowingData Guide to Design, Visualization, and Statistics. Indianapolis, IN: Wiley.
- Yau, Nathan. 2013. Data Points: Visualization that Means Something. Hoboken, NJ: Wiley.
All write-ups, reviews, documentation and other written material must be original and may not be derived from other sources. We plan on having a relaxed collaboration policy for this course. However, you should always be clear on what part of the work you hand in is your own, what parts come from other sources, and what parts are collaborative. Generally if you are exchanging information through a written, video, or visual medium then it rises to the level of something that you should report when you hand in your assignment. Exceptions would include getting help with a simple syntax error or where to find a certain button in a program. You will not be penalized for collaboration; it is just important for us to know to get a better sense of what you and your fellow students know. Failure to cite work that you draw from other sources is a violation of Bowdoin’s Academic Honor Code.
I. Defining Data though Data Collection, Access, Readability, and Reliability
A. Graph method and analysis
Mon 1/20 Digital and Computational Social Sciences: Introductions to Data
Wed 1/22 Social Data Sciences; or, Is everything “data” and is “everything” data?
Pentland, Alex. 2013. “The Data-Driven Society.” Scientific American 309 (4) (October 1): 78–83. doi:10.1038/scientificamerican1013-78.
Yau, Nathan. 2013. “Understanding Data.” In Data Points: Visualization That Means Something, 1–42. Hoboken, N.J.: Wiley.
Flip Classroom Assignments
Create a Twitter account
How to post to WordPress
How to set up your profile in WordPress
Install Python (for PC users)
Assignment: Blog post: why you chose your topic and what you expect to find.
Wed 1/22 LAB: Introduction to Data Scraping with Python & ScraperWiki
Yau, Nathan. 2011. “Handling Data.” In Visualize This: The FlowingData Guide to Design, Visualization, and Statistics, 21–52. Indianapolis, Ind: Wiley.
Mon 1/27 How the Internet Works, from 1991 to Today
Berners-Lee, Tim, Robert Calliau, Ari Loutonen, Henrik Frystk Nielsen, and Arthur Secret. 1994. “The World Wide Web.” Communications of the ACM 37 (8): 76–82.
Marwick, Alice Emily. 2013. Excerpts from “A Cultural History of Web 2.0.” In Status Update: Celebrity, Publicity, and Branding in the Social Media Age, 21-35, 59–72. New Haven: Yale University Press.
Flip Classroom Assignment
Basics of Excel
Wed 1/29 How We Make Sense of Data in Visualization
Tufte, Edward R. 2011. “Visual & Statistical Thinking: Displays of Evidence for Making Decisions.” In Envisioning Information, 27–54. Cheshire, Conn.: Graphics Press.
Yau, Nathan. 2013. “Visualization: The Medium.” In Data Points: Visualization That Means Something, 43–90. Hoboken, N.J.: Wiley.
Wed 1/29 LAB: Excel and statistics
Mon 2/3 Keeping it Real and Honest: Representing Data
Kurgan, Laura. 2013. “Representation and the Necessity of Interpretation.” In Close Up at a Distance: Mapping, Technology, and Politics, 19–38.
———. 2013. “Million-Dollar Blocks: The ‘Most Phenomenal’ Fact of All.” In Close Up at a Distance: Mapping, Technology, and Politics, 186–204.
Yau, Nathan. 2013. “Representing Data.” In Data Points: Visualization That Means Something, 91–134. Hoboken, N.J.: Wiley.
Projects to review:
Wed 2/5 Privacy, Now: from the NSA to Facebook, Snapchat to Anonymous
Cohen, Julie E. 2012. “Introduction: Imagining the Networked Society.” In Configuring the Networked Self: Law, Code, and the Play of Everyday Practice, 3–31. New Haven: Yale University Press.
Illinsky, Noah P.N. 2010. “On Beauty.” In Beautiful Visualization: Looking at Data through the Eyes of Experts, edited by Julie Steele and Noah P.N. Illinsky, 1–15. Beijing, China: O’Reilly.
Blog post: what is data and how does representation matter in regard to your dataset.
Wed 2/5 LAB: Using R to create histograms, pie charts, and line graphs
Yau, Nathan. 2011. “Visualizing Patterns over Time.” In Visualize This: The FlowingData Guide to Design, Visualization, and Statistics, 91–134. Indianapolis, Ind: Wiley.
Mon 2/10 Defining “Big Data” and Redefining Privacy
boyd, danah, and Kate Crawford. 2012. “Critical Questions for Big Data.” Information, Communication & Society 15 (5): 662–679. doi:10.1080/1369118X.2012.678878.
Crawford, Kate. 2013. “Think Again: Big Data.” Foreign Policy, May 9.
Vis, Farida. 2013. “A Critical Reflection on Big Data: Considering APIs, Researchers and Tools as Data Makers.” First Monday 18 (10) (October 7). doi:http://dx.doi.org/10.5210%2Ffm.v18i10.4878. http://journals.uic.edu/ojs/index.php/fm/article/view/4878.
Wed 2/12 The Search Engine and the Search for Privacy
Pariser, Eli. 2012. “Introduction.” In The Filter Bubble: How the New Personalized Web Is Changing What We Read and How We Think, 1–20. New York, N.Y.: Penguin Books/Penguin Press.
———. 2012. “The Race for Relevance.” In The Filter Bubble: How the New Personalized Web Is Changing What We Read and How We Think, 21–46. New York, N.Y.: Penguin Books/Penguin Press.
Stray, Jonathan. 2012. “Are We Stuck in Filter Bubbles? Here Are Five Potential Paths Out.” Nieman Journalism Lab. July 11. http://www.niemanlab.org/2012/07/are-we-stuck-in-filter-bubbles-here-are-five-potential-paths-out/.
Wed 2/12 LAB: Using R for Correlation and Causation
Yau, Nathan. 2011. “Visualizing Relationships.” In Visualize This: The FlowingData Guide to Design, Visualization, and Statistics, 179–226. Indianapolis, Ind: Wiley.
Mon 2/17 On Digital Publics of Opening…or Not
Donovan, Gregory. 2014. “Opening Proprietary Ecologies: Participatory Action Design Research with Young People.” In Methodological Challenges When Exploring Digital Learning Spaces in Education, edited by K.B. Vasbø and G.B. Gudmundsdottir. New York: Sense.
Odewahn, Andrew. 2010. “Visualizing the US Senate Social Graph (1991-2009).” In Beautiful Visualization: Looking at Data through the Eyes of Experts, edited by Julie Steele and Noah P.N. Illinsky, 123–142. Beijing, China: O’Reilly.
Golbeck, Jennifer. 2013. “Introduction; Nodes, Edges, and Network Measures.” In Analyzing the Social Web, 1–8, 9–24. Waltham, MA: Morgan Kaufmann/Elsevier.
Assignment: Blog comment
Projects to review:
CartoDB. 2014. “Beyonce Releases Her New Album Online.” Beyonce Releases Her New Album Online: Geotagged Tweets Mentioning Beyonce’s Online Self-titled Album, December 12-13, 2013. Accessed January 8. http://srogers.cartodb.com/viz/337d9194-6458-11e3-85b5-e5e70547d141/embed_map?title=true&description=true&search=false&shareable=false&cartodb_logo=false&layer_selector=false&legends=false&scrollwheel=true&sublayer_options=.
Fraas, Mitch. 2013. “Mapping Books: The Dispersal of the Medieval Libraries of Great Britain”. Blog. Mapping Books. http://mappingbooks.blogspot.com/2013/11/the-dispersal-of-medieval-libraries-of.html.
Wed 2/19 Redefining Publics through Data
Johnson, Jeffrey Alan. 2013. “From Open Data to Information Justice”. SSRN Scholarly Paper ID 2241092. Rochester, NY: Social Science Research Network. http://papers.ssrn.com/abstract=2241092.
Silver, Nate. 2012. Selections from The Signal and the Noise: Why So Many Predictions Fail — but Some Don’t. New York: Penguin Press HC, The.
Wed 2/19 LAB: Using R for Bayesian statistics
Mon 2/24 Public (Invisible) Labor
Irani, Lilly. 2012. “Microworking the Crowd.” Limn 2. http://limn.it/microworking-the-crowd/.
Ross, Andrew. 2013. “In Search of the Lost Paycheck.” In Digital Labor: The Internet as Playground and Factory, edited by Trebor Scholz, 13–32. Digital Labor: Routledge.
Golbeck, Jennifer. 2013. “Network Structure and Measures.” In Analyzing the Social Web, 25–44. Waltham, MA: Morgan Kaufmann/Elsevier.
Assignment: Blog post: reflect on your work in lab to turn your Twitter dataset
Wed 2/26 Convincing the Public / Publics Convincing Themselves
Baym, Nancy K., and danah boyd. 2012. “Socially Mediated Publicness: An Introduction.” Journal of Broadcasting & Electronic Media 56 (3): 320–329. doi:10.1080/08838151.2012.705200.
Lee, Newton. 2013. “Consumer Privacy in the Age of Big Data.” In Facebook Nation, 61–66. Springer New York.
Yau, Nathan. 2013. Selections from “Exploring Data Visually.” In Data Points: Visualization That Means Something, 135–153. Hoboken, N.J.: Wiley.
Golbeck, Jennifer. 2013. “Network Visualization.” In Analyzing the Social Web, 45–62. Waltham, MA: Morgan Kaufmann/Elsevier.
B. Spatial Analysis
Wed 2/26 LAB: Introduction to Google maps & Social Explorer
IV. The Life of Code
Mon 3/3 Code Is…
Grossman, Lev, Jay Newton-Small, Jessica Roy, and Laura Stampler. 2013. “The Deep Web.” Time 182 (20) (November 11): 26-33.
Lessig, Lawrence. 2006. “Code Is Law.” In Code: Version 2.0, 1–8. New York: Basic Books.
———. 2006. “Regulating Code.” In Code: Version 2.0, 61–80. New York: Basic Books.
Yau, Nathan. 2013. Selections from “Exploring Data Visually.” In Data Points: Visualization That Means Something, 176-200. Hoboken, N.J.: Wiley.
Wed 3/5 Seeing Code in Space and Place
Graham, Stephen D. N. 2005. “Software-Sorted Geographies.” Progress in Human Geography 29 (5) (October 1): 562–580. doi:10.1191/0309132505ph568oa.
Jabbour, Eddie, and Julie Steele. 2010. “Mapping Information: Redesigning the New York City Subway Map.” In Beautiful Visualization: Looking at Data through the Eyes of Experts, edited by Julie Steele and Noah P.N. Illinsky, 69–90. Beijing, China: O’Reilly.
Shanley, Lea, Ryan Burns, Zachary Bastian, and Edward S. Robson. 2013. “Tweeting Up a Storm: The Promise and Perils of Crisis Mapping.” Photogrammetric Engineering & Remote Sensing (October): 866–880.
Projects to review:
Rahul. 2013. “Occupy Sandy and Emerging Forms of Social Organization.” Ashley Dawson. Accessed December 18. http://ashleydawson.info/2013/05/11/occupy-sandy-and-emerging-forms-of-social-organization/.
Occupy Sandy Spokes Council. 2012. “Occupy Sandy Recovery.” Occupy Sandy Recovery. http://occupysandy.net/.
Wed 3/5 LAB: Examining Your Own Data with GoogleMaps and Social Explorer
Mon 3/7-23 Spring Break: No Classes or Labs
Mon 3/31 Code Undone
Coleman, Gabriella. 2012. “Am I Anonymous?” Limn (2). http://limn.it/am-i-anonymous/.
Wark, McKenzie. 2013. “Considerations on a Hacker Manifesto.” In Digital Labor: The Internet as Playground and Factory, edited by Trebor Scholz, 69–76. Digital Labor: Routledge.
Golbeck, Jennifer. 2013. “Tie Strength.” In Analyzing the Social Web, 63–74. Waltham, MA: Morgan Kaufmann/Elsevier.
Projects to review:
Baldwin, J.R. 2012. “Top Needs of Occupy Sites | Limn.” Limn 2. http://limn.it/top-needs-of-occupy-sites/.
Wed 4/2 Whose Code? Whose Culture?
Kitchin, Rob, and Martin Dodge. 2014. “Introducing Code/Space.” In Code/Space: Software and Everyday Life, 3–22. Cambridge, MA: MIT Press.
Donovan, Gregory, and Cindi Katz. 2009. “Cookie Monsters: Seeing Young People’s Hacking as Creative Practice.” Children, Youth and Environments 19 (1): 197–222.
Blog post: speak to your GoogleMaps and Social Explorer maps related to your datasets.
Wed 4/2 LAB: Using Excel and R with Your Data
V. Sociality & Cyborgembodiment
Mon 4/7 Our Bodies, Our(Social Media)selves: Cyborg Embodiment
Nakamura, Lisa. 2010. “Race and Identity in Digital Media.” In Mass Media and Society, edited by James Curran, 336–347. New York: Bloomsbury.
Neff, Gina. 2013. “Why Big Data Won’t Cure Us.” Big Data 1 (3) (September): 117–123. doi:10.1089/big.2013.0029.
Yau, Nathan. 2013. Selections from “Visualizing with Clarity.” In Data Points: Visualization That Means Something, 201–220. Hoboken, N.J.: Wiley.
Golbeck, Jennifer. 2013. “Trust.” In Analyzing the Social Web, 75–90. Waltham, MA: Morgan Kaufmann/Elsevier.
Projects to review: TBD
C. Network Analysis
Wed 4/9 Professors Gieseking and Gaze will be at conferences – read/lab on your own
Golbeck, Jennifer. 2013. “Understanding Structure through User Attributes and Behavior.” In Analyzing the Social Web, 91–106. Waltham, MA: Morgan Kaufmann/Elsevier.
———. 2013. “Building Networks.” In Analyzing the Social Web, 107–124. Waltham, MA: Morgan Kaufmann/Elsevier.
Yau, Nathan. 2013. Selections from “Visualizing with Clarity.” In Data Points: Visualization That Means Something, 221–240. Hoboken, N.J.: Wiley.
Assignment: Blog post: write about the graphs that you created from your data in lab.
Wed 4/9 LAB: Gephi Quick Start Tutorial on Les Mis (on your own)
Mon 4/14 Identity Online
Gray, Mary L. 2007. “From Websites to Wal-Mart: Youth, Identity Work, and the Queering of Boundary Publics in Small Town, USA.” American Studies 48 (2) (July 1): 49–59.
Nakamura, Lisa. 2011. “Syrian Lesbian Bloggers, Fake Geishas, and the Attractions of Identity Tourism” Hyphen: Asian America Unabridged (July 15). http://www.hyphenmagazine.com/blog/archive/2011/07/syrian-lesbian-bloggers-fake-geishas-and-attractions-identity-tourism.
Krebs, Valdis. 2010. “Your Choice Reveals Who You Are: Mining and Visualizing Social Patterns.” In Beautiful Visualization: Looking at Data through the Eyes of Experts, edited by Julie Steele and Noah P.N. Illinsky, 103–122. Beijing, China: O’Reilly.
Golbeck, Jennifer. 2013. “Building Networks.” In Analyzing the Social Web, 107–124. Waltham, MA: Morgan Kaufmann/Elsevier.
Wed 4/16 Creating Sociality, Producing New(s) Media
Miller, Greg. 2013. “Here’s How Memes Went Viral — In the 1800s.” Wired Science: MapLab. November 4. http://www.wired.com/wiredscience/2013/11/data-mining-viral-texts-1800s/.
Perer, Adam. 2010. “Finding Beautiful Insights in the Chaos of Social Network Visualizations.” In Beautiful Visualization: Looking at Data through the Eyes of Experts, edited by Julie Steele and Noah P.N. Illinsky, 157–174. Beijing, China: O’Reilly.
Golbeck, Jennifer. 2013. “Entity Resolution and Link Prediction.” In Analyzing the Social Web, 125–150. Waltham, MA: Morgan Kaufmann/Elsevier.
Golbeck, Jennifer. 2013. “Propagation in Networks.” In Analyzing the Social Web, 151–168. Waltham, MA: Morgan Kaufmann/Elsevier.
Flip Classroom Assignments
Netvizz your dataset from Facebook
Wed 4/16 LAB: Visualizing and Reading Your Own Facebook Social Network
VI. Power & Format
Mon 4/21 Remix
Lessig, Lawrence. 2009. “Cultures of Our Past.” In Remix: Making Art and Commerce Thrive in the Hybrid Economy, 23–33. New York: Penguin Books.
———. 2009. “Cultures of Our Future.” In Remix: Making Art and Commerce Thrive in the Hybrid Economy, 34–35. New York: Penguin Books.
———. 200c. “RO, Extended.” In Remix: Making Art and Commerce Thrive in the Hybrid Economy, 36–50. New York: Penguin Books.
———. 2009. “RW, Revived.” In Remix: Making Art and Commerce Thrive in the Hybrid Economy, 51–83. New York: Penguin Books.
Projects to review:
“DOCC 2013: Dialogues on Feminism and Technology | FemTechNet.” 2013. Accessed December 3. http://femtechnet.newschool.edu/docc2013/.
Assignment: Blog comment
Weds 4/23 Media Archaeology+
Anderson, Chris. 2012. “The Long Tail.” In The Social Media Reader, edited by Michael Mandiberg, 137–154. New York: New York University Press.
Sterne, Jonathan. 2006. “The MP3 as Cultural Artifact.” New Media & Society 8 (5) (October 1): 825–842. doi:10.1177/1461444806067737.
Wed 4/23 LAB: Build a Network Analysis from Your Twitter Data Set
Mon 4/28 Visualization as Power & Format
Davison, Patrick. 2012. “The Language of Internet Memes.” In The Social Media Reader, edited by Michael Mandiberg, 120–136. New York: New York University Press.
Hagy, Jessica. 2010. “Visualization: Indexed.” In Beautiful Visualization: Looking at Data through the Eyes of Experts, edited by Julie Steele and Noah P.N. Illinsky, 353–368. Beijing, China: O’Reilly.
Assignment: Blog comment
Weds 4/30 Spacetime to work on final projects
Yau, Nathan. 2013. “Designing for an Audience.” In Data Points: Visualization That Means Something, 241–276. Hoboken, N.J.: Wiley.
Wed 4/30 LAB: Spacetime to work on final projects
Mon 5/5 Final presentations
Wed 5/7 Final presentations
Wed 5/7 No lab
Mon 5/12 Papers due
Fri 5/17 Final exam at 9am