Dataffiti is a portmanteau that blends the words data and graffiti. I could have used the term daffiti but it sounds too much like “defeated,” so I opted to keep the “t.” As the tagline — “from pop culture to analytical insights” – suggests, the blend is meant to represent the application of data science techniques and tools to the world of popular or pop culture in order to gain insights into its aggregate structures and trends — the who and with whom, the what, the when and the where.
The terms popular culture or pop culture encompass a variety of definitions – both broad and narrow. Given the fact that I’ve combined the word data with (gr)affiti indicates that the my interests are narrower and will be focused on cultural products that are on or at least started out on the fringes of society before they reached the mainstream. Under this rubric, the types of projects to be consider include: text analysis of the lyrics in hip-hop and rap music; image analysis of graffiti and street art; social network analysis (SNA) of the casts of Bollywood movie, statistical analysis of the episodes of Man vs. Food and Diners, Drive-Ins and Dives; dynamic analysis of the influence networks among the “isms” (like cubism) of modern and contemporary art, to name a few.
The analysis will also be supported and supplemented by a wide variety of visualizations encompassed by the terms “charts, tables, (statistical) graphs, geospatial maps and network graphs” supporting the statistical, temporal, geospatial, topical and network analysis, respectively. Most of the visualizations will employ the visualization framework detailed by Börner and friends. Like the base analysis, these visualizations will be generated either by visualization capabilities of Python and R or by specialized visualization capabilities of D3.js, Sci2, or the Processing programming language for the visual arts.
Crossman, Ashley. http://sociology.about.com/od/P_Index/g/Popular-Culture.htm
Morretti, Franco (2007). Graphs, Maps and Trees: Abstract Models for Literary History. London: Verso.
Manovich, Lev. Manovich.net & http://lab.softwarestudies.com/p/cultural-analytics.html
Katy Börner et al. at the Cyberinfrastructure for Network Science Center. Major works include: (2010) Atlas of Science: Visualizing What We Know; (2014) Visual Insights: A Practical Guide to Making Sense of Data with David Polley; and (2015) Atlas of Knowledge: Anyone Can Map. All are published by MIT Press.