digital humanities ++

VIS 241: Topics in Contemporary Critical Theory
Spring 2011
Visual Arts Department | UCSD

TIme: Tuesday, 3:30-6:20pm
Location: VIS 366 or Calt2 conference rooms (TBA)

The syllabus for this class is online at www.manovich.net
As the course progresses, the additional materials will be added to the course web site.

instructor: Dr. Lev Manovich
office hours: Tuesday 2-3pm @Cafe Roma, or by appointment
email: manovich@ucsd.edu
lab: www.softwarestudies.com

Readings:
All readings for this class will be available online at no charge.

Software:
The class will use free publicly available software as well as software tools developed by Software Studies Initiative.

Spring 2009 Topic: Digital Humanities++

Course description

“How does the notion of scale affect humanities and social science research?
Now that scholars have access to huge repositories of digitized data—far more than they could read in a lifetime—what does that mean for research?”
The description of joint NEH/NSF Digging into Data competition (2009) organized by Office of Digital Humanities at the National Endowment of Humanities (the U.S. federal agency which funds humanities research).

“The next big idea in language, history and the arts? Data.”
New York Time, November 16, 2010.

Over the last few years, digital humanities - use of computational tools for cultural analysis - has been rapidly expanding, with growing number of grants, panels and presentations at conferences, and media coverage. (For example, New York Times is doing a series of articles on digital humanities, with 5 articles already pusblished since November 2010.) However, almost all projects so far focused on text and spatial data (literature and history departments). With a few exceptions, other fields including art history, visual culture, film and media studies, musicology, and new media have yet to start using computational methods. But even in social sciences, the disciplines which are dealing with culture (media studies, cultural sociology, anthropology) and which employ quantitative methods, still did not discover full possibilities of "cultural computation." As a result, the opportunities currently are immense, and its exiting time to enter the field.

This graduate seminar explores the concepts, methods, and tools of computational cultural analysis, with a particular focus on the analysis of visual and interactive media. (This is also the focus of our lab's cultural analytics research).

We will also discuss cultural, social and technical developments which gave us "large cultural data" (digitization by cultural institutions, media archives, social media) and which placed "information" and "data" in the center of contemporary social and economic life (the concepts of information society, network society, software society)

We will also critically interferometer the fundamental paradigms developed by modern and contemporary societies to analyze patterns in data - statistics, visualization, data mining. This will help us to employ computational tools more reflexively. At the same time, the practical work with these tools will help us to understand how they are used in society at large - the modes of thinking and inquiry they enable, their strengths and weaknesses, the often unexamined assumptions behind their use.
(This approach can be called "reflexive digital humanities.")

We will discuss theoretical issues raised by digital humanities: data (selected artifacts vs. sample vs. complete population) meaning vs. pattern; artifacts vs. cultural processes; analysis of interactive media; combining established humanities methods with software methods.

The class assumes that while software methods can be used in the service of existing humanities questions and approaches, they also have radical potential to challenge existing concepts and research paradigms, and lead to new types of questions. To engage this potential, we have to start by considering "contemporary techniques of control, communication, representation, simulation, analysis, decision-making, memory, vision, writing, and interaction" enabled by software in society at larger. (Manovich, Introduction to Software Takes Command.) Projecting these techniques onto the problem of cultural analysis will tell us what digital humanities can be. (Let's call it digital humanities++).

The seminar combines readings, discussion, exercises to learn tools and techniques, and collaborative work in groups to conduct original digital humanities projects. Students will be able to use the data sets already assembled by Software Studies Initiative as well as the unique supervisualization HIPerSpace system.

The concepts, developments and trends to be discussed in class:

established humanities methods vs computational analysis of culture:
humanities vs. digital humanities
traditional methods and assumptions of cultural criticism vs. quantitative cultural analysis
cultural analytics, cultural informatics, cultrunomics
software studies
software as medium for thinking

cultural data:
information society / network society / software society
information growth
big cultural archives (Goggle books, archive.org, artstor, museum APIs, media archives, etc.)
metadata
faceted search
new scale of cultural production (pro and pro-ams)
web, web 2.0, semantic web
social media
long tail
user-generated content
folksonomy vs taxonomy
crowd sourcing
API
data politics
creative commons
online tools for aggregating and analyzing data

visualization:
scientific visualization
information visualization
information design
visual analytics
aristic visualization
visualization methods and techniques for humanities and media studies

data analysis:
image processing / computer vision
text mining
GIS
explaratory data analysis (software: Mondrian)
descriptive statistics (measures of centrality and dispersion)
selected concepts in multivariate statistics
data mining
the ideology of data segmentation/clustering
discrete categories/clusters vs. fuzzy/overlapping categories

The presentation of topics and techniques is organized to follow the conceptual stages of a typical digital humanities project:
locating the source of data and obtaining the data;
visualization to explore the data set(s);
analysis of data with computational techniques.

Student background
Students are not expected to have any special technical background.

Practical work: goals and tools

Based on the already completed case studies n our lab, in this class we will continue developing analytical and visualization techniques appropriate for working with still and time-based visual media such as visual art, graphic design, photography, fashion/street styles, feature films, animation, motion graphics, use-generated video, gameplay video recordings, web design, etc. (Students interested to work with other media - text, sound, maps - will have opportunity to focus on their areas.) We will also have access to state of the art supervisualization system at Calit2 (HIPerSpace) to explore large data sets.

The tools used in the class include ImageJ open-source image processing software, Google Docs, and other applications.

One of the goals of this class is to explore how the community-assigned semantic descriptions of media artifacts can be combined with automatic computer analysis of their visual structure and content. Therefore, some of our practical work will involve collaborative annotation of sets of cultural artifacts.

Practical work: credit system

This class will follow the approach and the credit system which is more close to the science than the traditional humanities. Instead of writing individual course papers we will work on collaborative research projects which will be presented online, and hopefully lead to publications.
Similar to the sciences, we will also use a systematic approach to giving the students credit for their participation:

1.The students will be fully credited in any publications and public/web presentations which result from the work conducted in this class.
2. Students can also list all publications which use the research which was conducted in this class (in which a student was directly involved in) on her/his CV.
3. The work done in this class can also potentially lead to joint publications between the instructor and the students with student's listed as co-authors on the papers.

Here is an example of a group student project done in this course in 2009.

One of the participants in this project later used the same approaches to create a new proejct which led to a publication: Tara Zepel. Cultural Analytics at Work: The 2008 U.S. Presidential Online Video Ads. In Video Vortex Reader II: Moving Images Beyond YouTube, edited by Geert Lovink and Somers Miles, 234-249. Amsterdam: Institute of Network Cultures, 2011.

Requirements

1.Consistent class attendance. You are allowed to miss one class meeting without an excuse. Missing any additional classes without proper excuse (doctor's notice) will lower your final grade half a letter grade for each class missed. Chronic lateness counts as absence.

2. Reading the assigned materials before each class meeting. If any additional online resources are assigned, you should go through them before the class meeting as well.

3. Active participation in class discussions.

4. Timely completion of the individual / group assignments.
No late assignments will be accepted. If the group does not complete its assigned work on time, all group participants will be penalized equally.

5. Participation in a group project. The project will be judged using the following criteria:

(1) is it meaningful and original, i.e. does it reveal new cultural patterns which are not already obvious or known?
(2) is it visually compelling?
(3) is is technically competent?
(4) you are available to provide convincing interpretations of the patterns you found - ideally based by additional research?

The project should be presented in the form of a web page(s) or site.

More detailed information and requirements for the group projects will be provided later in the quarter.

Grading
Each component of the class (1- 5) is equally important and will be counted equally in determining the final grade.

Schedule

How to prepare for each class:
the assigned articles will be discussed in class, so read them carefully;
you are also responsible for familiarizing yourself with all the terms listed for each week (if you dont the term, then do look at linked wikipedia article; but even if you know the term, you will still find interesting info and latest developments in the linked article) and visiting linked websites of projects, instituitons, and companies

note: only some of the readings are currently filled in, mostly for the first classes - others will be added later
note: in reading wikipedia articles, focus on concepts, origins of terms, and interesting links - you can skip more technical parts

---------------------------------------------------------------------------------------------

1 / 3.29.2011 / Introduction: analyzing cultural paterns with graphical and quantitative techniques

lecture references:

MacCloud, Scott. Understanding Comics. 1993.
Moretti, Franco. a chapter from Graphs, Maps, Trees. 2005.
cultural analytics. 2007-

---------------------------------------------------------------------------------------------
2 / 4.5.2011 / born-analog cultural data / describing, organizing, and presenting cultural data

read before class - articles/book chapters:

Tooling Up for Digital Humanities: Digitization. Stanford University, 2011.
Manoff, Marlene. Theories of the Archive from Across Discipline. 2004.
Shirky, Clay. Ontology is Overrated. 2005.
Williams, Dmitri, Nicole Martins, Mia Consalvo and James D. Ivory. The virtual census: representations of gender, race and age in video games. 2009. (Example of content analysis method.)

recommended:

Lars Marius Garshol. Metadata? Thesauri? Taxonomies? Topic Map! 2004.

Program of Museums and the Web 2011 conference

read before class - terms:
digital repository
digital curation
metadata
folksonomy
controlled vocabulary
Folksonomies and Tags
taxonomy
crowd sourcing
collective_intelligence
mass collaboration
API
relational database
content analysis

discuss in class:

grants to work with large digital archives:
Digging Into Data 2011 competition
other relevant NEH grants

visual representations of knowledge structures - hustorical and contemporary examples:
Place and Space exhibition (2011)

The largest directory of websites put together by human editors - an interesting example of a contemporary taxonomy:
Open Directory Project

examples of large data sets services:
infochimps

museums and the web - current research:
museums and the web 2011 conference program

examples of museums API:
Brooklyn Museum API application gallery
Powerhouse museum API

examples of interesting museum / digital collections interfaces:
Mace project
SFMOMA Artscope
Powerhouse museum australian dress register
LACMA collection remix
Natural Science Museum of Barcelona heat map interface
Google Art Project
Luna digital image collection software and collections
The virtual museum tour guide (BA thesis)

examples of interesting collection interfaces - artistic and commercial companies:
0xdb.org
moviebarcode
Getty Images Moodstream

selected large digital archives:
archive list
Europeana
Magnum Photos
Chronicling America: Historic American Newspapers
Hathi Trust Digital Library
Google Books

example of collaborative data annotation/creation cultural/academic projects:
Steve.museum
cinemetrics.lv
Movie Tagger (USC)
(and of course, you can upload image set on Flickr and tag it)

annotation software for video:
anvil

case study: local initiative:
Balboa Park Online Collaborative

---------------------------------------------------------------------------------------------
3 born-digital cultural data / publishing, gathering, aggregating, searching data

artices:
Latour, Bruno. Beware your imagination leaves digital traces. Times Higher Literary Supplement, 6th April 2007.
Manovich, Lev. How to Follow Global Digital Cultures, or Cultural Analytics for Beginners. Deep Search, ed. Felix Stalder and Konrad Becker. Transaction Publishers ( English version) and Studienverlag (German version), 2009. / sources of digital data
Gauntlett, David. Media Studies 2.0. 2007; update 2011.

terms:

born digital
digital footprint
web 2.0
social media
social network service
the long tail
user-generated content
mass_collaboration

Web search engine (read: How web search engines work)
semantic web

Collaborative_filtering
faceted classification

examples of a digital preservation project:
Library of Congress Preserving Digital Culture

example of data agregation software:
needlebase.com

example of cultural data aggregation:
one million syllabi

examples of innovative social media applications - "ambient collective media":
Photosynth. 2006.
John Battelle. Why Color Matters: Augmented Reality And Nuanced Social Graphs May Finally Come of Age. March 23, 2011.

Internet use statistics and trends:
Pew Internet

video:
Wesch, Michael. Web 2.0 (YouTube video, 2007).

---------------------------------------------------------------------------------------------

4 social media / network culture: theoretical perspectives and case studies

theory and case studies: articles / book chapters:

Benkler, Yochai. Peer Production and Sharing. Chapter 3 from The Wealth of Networks. 2006.

Varnelis, Kazys. The Rise of Network Culture. 2007.

Biljana Kochoska Taneska. OTAKU — the living force of the social media network. 2009.
Visit the web sites refered in the article.

Ito, Mizuko. The rewards of non–commercial production: Distinctions and status in the anime music video scene. 2010.

examples of recent quantiative analysis of social media and social networks:

Wikipedia Editor_Trends_Study

100-publishers-create-most-of-bittorrent-content

recommended:

Heather A. Horst, Becky Herr-Stephenson, and Laura Robinso. Media Ecologies. Chapter from Digital Youth Research project. 2008.

links to visit:

First Monday journal
New Media and Society journal
Institute of Network Cultures

Web analytics

---------------------------------------------------------------------------------------------
5 quantity and quality: methods for studing society and culture / interactive visualization

readings:

boyd, danah, Scott Golder, and Gilad Lotan. Tweet Tweet Retweet. 2010.
presenter: Tongyun

Kwak, Haewoon, Changhyun Lee, Hosung Park, and Sue Moon. What is Twitter, a Social Network or a News Media? 2010. (You can skip technical parts; focus on methods, research questions, and findings).
presenter: Benjamin

Bourdieu, Pierre. A Social Critique of the Judgement of Taste. Chapter 5. 1979. English translation: 1984.
presenter: Lesley

Latour, Bruno. Tarde’s idea of quantification. In Mattei Candea (editor) The Social After Gabriel Tarde: Debates and Assessments, Routledge, London, pp. 145-162. 2009.
presenter: Matthew

view:
Hans Roling, TED lecture, 2006

examples of free cisualization tools coupled with data sets:
gapminder
Google Insights for Search
Google Books Ngrams viewer

---------------------------------------------------------------------------------------------
6 statistics history / descriptive statistics / stylometry / explaratory data analysis

readings:

http://en.wikipedia.org/wiki/Stylometry

Selected chapters from Anthony Kenny. Computation of Style (1982). file: The_Computation_of_Style.pdf
(skip the parts on how to manually calculate statistical values).

From: Studying Contemporary American Film: A Guide to Movie Analysis, by Thomas Elsaesser and Warren Buckland, pp. 101-116.

register to use manyeyes

install Mondrian software on your computer

free web statistical software: http://www.wessa.net/

recommended:

Juola, Patrick (2006). Authorship Attribution. Foundations and Trends in Information Retrieval 1:3.

--------------------------------------------------------------------------------------------
7 cultural analytics: digital image analysis and visualization methods for the study of visual culture and media

readings:

Lev Manovich. What is Visualization? 2010.

examples of cultural analytics projects

resources: online galleries/blogs:

visualcomplexity

infoaesthetics

Tooling Up for Digital Humanities: Visualization. Stanford University, 2011.

content-based search:
Victoria and Albert Museum Search by Color

---------------------------------------------------------------------------------------------
8 data mining / text mining / network analysis / GIS and spatial analysis

text analytics:

Tooling Up for Digital Humanities: Text Analysis. Stanford University, 2011.

geospatial analysis:

Tooling Up for Digital Humanities: Spatial Analysis. Stanford University, 2011.

What is Spatial History?

network analysis:

Social network (wikipedia article)

analytics:

Thomas H. Davenport and Jeanne G. Harris. What People Want (and How to Predict It). MIT Sloan Managment Review, January 9, 2009.

terms:
data mining
web mining
text analytics
Geographic information system
Spatial analysis

examples of geospatial projects in humanities:

Stanford The Spatial History Project