What is Dataffiti?


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.


For those familiar with the field of Digital Humanities, you may recognize that these topics and the associated analysis follow in the footsteps of Franco Moretti’s work on literary history, especially his essay on “Graphs, maps, and trees,” and Lev Manovich’s cultural analytics, principally the work he and his team have done on the visual analysis of large collections of images (e.g. Instagram images, manga pages, and Impressionist paintings). Unlike Moretti, but like Manovich, most of the (pop culture) data to be analyzed comes from the Web, albeit in a variety of shapes, forms and fashions – including online databases, APIs, and the underlying HTML source of associated Web pages.   As the sample topics indicate, the data science analytical techniques and tools to be employed run the gamut from standard statistical analysis, to machine learning and data mining, to natural language analysis, to image processing and computer vision, and to (social) network analysis. The analysis will rely on two basic kinds of tools including: Programming Languages – Python, R, and JavaScript; and specialized software for Social Network Analysis (SNA) – Pajek, UCINet, Gephi, and ORA.


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.


Wikipedia. https://en.wikipedia.org/wiki/Popular_culture

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.

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