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How and why study big
visual cultural data

Dr. Lev Manovich
Professor, CUNY Graduate Center
manovich.lev@gmail.com
softwarestudies.com

Fall 2012 version


                        softwarestudies.com   1
softwarestudies.com   2
Software Studies Initiative - 2007

NEH Office for Digital Humanities - 2008

NEH Humanities High Performance Computing - 2008

NEH/NSF Digging Into Data competition - 2009

Computational Social Science - 2009

Culturnomics and Google n-gram viewer - 2010

New York Times: “The next big idea in language,
history and the arts? Data.”- 2010

                              softwarestudies.com   3
How can we take advantage of unprecedented
amounts of cultural data available on the web
and digitized cultural heritage to begin analyzing
cultural processes in new ways?

How does computational analysis of the
massive cultural datasets and real-time flows
can help us to develop theories and methods in
humanities adequate for the scale and speed of
the 21st century global networked digital
culture ?




                           softwarestudies.com       4
NEH/NSF Digging into Data competition (2009):

“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?”




                          softwarestudies.com   5
Why study
big cultural data ?




               softwarestudies.com   6
1 study societies through the social media
traces (social computing)

2 more inclusive understanding of cultural
history and present (using much larger
samples)

3 detect large scale cultural patterns




                            softwarestudies.com   7
4 generate multiple maps of the same cultural
data sets (multiple “landscapes”)

5 the best way to follow global professionally
produced digital culture; understand new
developed cultural fields (“X” design)

6 map cultural variability and diversity




                           softwarestudies.com   8
softwarestudies.com   9
Example - graph from Ted Underwood, “The Differentiation of Literary
and nonliterary diction, 1700-1900.” Data: 3,724 18th century volumes,
using 10,000 most frequent words (excluding proper nouns).

                                          softwarestudies.com            10
modern (19th-20th centuries) social and
cultural theory: describe what is similar
(classes, structures, types) / statistics
(reduction)

computational humanities and social science
should focus on describing what is different /
variability / diversity

“from data to knowledge” is wrong. In the
study of culture, we need to go from our
(incomplete, biased) knowledge to actual
cultural data


                          softwarestudies.com    11
“We are no longer interested in the conformity
of an individual to an ideal type; we are now
interested in the relation of an individual to the
other individuals with which it interacts...
Relations will be more important than
categories; functions, which are variable, will
be more important than purposes; transitions
will be more important than boundaries;
sequences will be more important than
hierarchies.”

Louis Menand on Darvin, 2001.



                            softwarestudies.com      12
Visualization: Thinking
without “large” categories




              softwarestudies.com   13
Manual De Landa:
“The ontological status of assemblages, large
and small, is always that of unique, singular
individuals.”

“Unlike taxonomic essentialism in which
genus, species and individuals are separate
ontological categories, the ontology of
assemblages is flat since it contains nothing
but differently scaled individual singularities.”

source: A New Philosophy of Society.

                            softwarestudies.com     14
Bruno Latour:
“The ‘whole is now nothing more than a
provisional visualization which can be
modified and reversed at will, by moving back
to the individual components, and then
looking for yet other tools to regroup the same
elements into alternative assemblages.”

source: “Tarde’s idea of quantification.” In
The Social After Gabriel Tarde: Debates and
Assessments.


                          softwarestudies.com     15
How to study big cultural
visual data in practice?
How to explore massive visual collections
(exploratory media analysis)?

Which data analysis and visualization
techniques are appropriate for non-technical
users? How to democratize data analysis?



                          softwarestudies.com   16
Our methodology:
media visualization

display complete
collection sorted using
metadata and/or extracted
features

              softwarestudies.com   17
infovis: data into pictures

mediavis: pictures into pictures




                  softwarestudies.com   18
left: scatter plot
right: media visualization (image plot) of the same data




                              softwarestudies.com    19
our media visualization software on 287 megapixel display (image: 1 million manga pages)
our media visualization software on newer
display wall with thin bezels
data: 4535 Time magazine covers)




                                            softwarestudies.com   21
mediavis - related research:
M. Worring, G.P. Nguyen. Interactive access to large
image collections using similarity-based visualization.
Journal of Visual Languages and Computing 19 (2008)
(submitted 2005).

Gerald Schaefer. Interactive Browsing of Image
Repositories. ICVG 2012.

Jing et al., Google Inc. Google Image Swirl: A Large-Scale
Content-Based Image Visualization System. WWW 2012.


                              softwarestudies.com         22
mediavis vs. normal
computer science approach:
borrow techniques from media art, digital art,
information visualization / for non-technical users

explore the possibilities of simplest techniques by
using them with media collections from every area
of humanities

use mediavis to challenge existing concepts and
assumptions of humanities

                           softwarestudies.com    23
Basic media visualization
techniques:
1 montage: sort images using metadata

2 slice: sample images and arrange using
metadata

3 image plot: automatically measure image
properties (features) and organize in 2D using
these measurements and metadata

                          softwarestudies.com    25
1
montage: sort images
using metadata




4535 Time covers, 1923-2009

                              softwarestudies.com   26
1 montage close up:   Time magazine covers, 1920s




                                  softwarestudies.com   27
1 montage close up:   Time magazine covers, 1990s-2000s




                                  softwarestudies.com     28
2
slice: sample images and arrange using metadata




4535 Time covers, 1923-2009. Each line is a vertical slice through the center of an image.


                                                softwarestudies.com                  29
Time coves slice close-up




                            softwarestudies.com   30
3 image plot: organize images using features and
(optionally) metadata




Image plots of 4535 Time covers, 1923-2009. X-axis = date; Y-axis = saturation mean.

                                              softwarestudies.com                  31
Time covers image plot close-up




                            softwarestudies.com   32
Comparing a number of image sets with image plots




Selected paintings by six impressionist artists. X-axis = mean saturation. Y-axis =
median hue. Megan O’Rourke, 2012.

                                                softwarestudies.com                   33
softwarestudies.com   34
visualizing video
collections:

use media visualization with a set of
keyframes

automatic selection of key frames
(for example, using free shot detection
software)




                          softwarestudies.com   35
Kingdom Hearts video game
62.5 hr. of game play, 29 sessions over 20 days.ys.
montage: 1 frame per 3 sec (22500 frames in total)




                              softwarestudies.com
softwarestudies.com   37
softwarestudies.com   38
11th Year (Dziga Vertov, 1928): first frame of every shot




                               softwarestudies.com
11th Year (Dziga Vertov, 1928): comparing first
and last frame in every shot (close-ups from
the larger visualization)




                             softwarestudies.com   40
Why use numbers?

Using numbers to describe
cultural artifacts allows to
replacing discrete
categories (words) with
continuos descriptions
(curves)
               softwarestudies.com   41
1 from timelines to graphs

2 better represent analog attributes
of cultural artifacts

3 map cultural landscapes (fuzzy /
overlapping / hard clusters?)

4 visualize cultural variability

5 discover new gropings
                     softwarestudies.com   42
1 from timelines to curves                Mark Rothko, 393 paintings (1927-1970).
X - year. Y - brightness mean. Hao Wang and Mayra Vasquez.




                                              softwarestudies.com
2 better represent analog attributes of cultural artifacts

Next slide:
close-up of a visualization showing average amount of
visual change (bar graph) in every shot in Vertov’s
11th year. Images above the bar: first frame of every
shot.


To measure visual change per shot:
1) calculate brightness mean of the difference image
between each two frames in the shot
2) add all means
3) divide by number of frames in the shot

                              softwarestudies.com
softwarestudies.com
3 the maps of cultural landscapes reveal fuzzy and
overlapping clusters - rather than discrete categories
with hard boundaries
                               softwarestudies.com       46
4 visualize the space of variations
600 variations of Google Logo, 1988-2009




                                           softwarestudies.com
softwarestudies.com   48
Studying large massive
data sets challenges our
existing theoretical
concepts and assumptions

example: what is “style”?


              softwarestudies.com   49
image plot of one million manga pages
x - standard deviation
y - entropy




                                        softwarestudies.com
softwarestudies.com   51
distribution of
million manga pages

x - standard deviation
y - entropy




                         softwarestudies.com   52
single short manga series
< 1000 pages




                            softwarestudies.com   53
776 Vincent van Gogh paintings. X - year/month. Y - brightness mean.




                                              softwarestudies.com      54
Current / recent projects
at softwarestudies.com:
6000+ paintings of French Impressionists

7000 year old stone arrowheads
(with UCSD anthropologist)




                         softwarestudies.com   55
samples from 4.7 million newspaper pages
collection from Library of Congress (UCSD
undergraduate students)

virtual world / game analytics (funded by NSF
Eager, with UCSD Experimental Games Lab)

comparing Art Now & Graphic design Flickr
groups (340,000 images)
(with CS collaborator from Laurence Berkeley
National Laboratory)




                          softwarestudies.com   56
Big project supported by Mellon Foundation
Grant, 2012-2015

- tools and workflows for working with image
and video collections using SEASR / MEANDRE
digital humanities workflow platform

- applications:
1) 1+ million images + millions of metadata
records from deviantArt (the largest social
network for user-created art - 20 M users, 240 M
artworks).
2) 1+ million manga pages.
3) thousands of hours TV poltical news and
online video
                          softwarestudies.com      57
Postscript:

digital humanities (working
with digitized collections of
historical artifacts)
vs. computational humanities
(using social web data)

               softwarestudies.com   58
“The capacity to collect and analyze massive amounts
of data has transformed such fields as biology and
physics. But the emergence of a data-driven
'computational social science' has been much slower.
Leading journals in economics, sociology, and political
science show little evidence of this field. But
computational social science is occurring in Internet
companies such as Google and Yahoo, and in
government agencies such as the U.S. National
Security Agency.”

“Computational Social Science.” Science, vol. 323, no.
6, February 2009.


                             softwarestudies.com     59
Massive amounts of cultural content and online
conversations, opinions, and cultural activities
(general and specialized social media networks;
personal and professional web sites ).
This data offers us unprecedented opportunities to
understand cultural processes and their dynamics
and develop new concepts and models which can be
also used to better understand the past.

Currently only analyzed by Google, Facebook,
YouTube, Bluefin labs, Echonest, and other
companies, and computer scientists working in
“social computing”- not yet by humanists.


                           softwarestudies.com   60
manovich.lev@gmail.com

softwarestudies.com




                  softwarestudies.com   61
Our free open source software tools for
analyzing and visualizing large image and
video collections, publications and
projects:

softwarestudies.com

The tools run on Mac, PC, Unix.

All media visualizations in this presentation
were created by members of Software


                       softwarestudies.com   62

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How and why study big cultural data v2

  • 1. How and why study big visual cultural data Dr. Lev Manovich Professor, CUNY Graduate Center manovich.lev@gmail.com softwarestudies.com Fall 2012 version softwarestudies.com 1
  • 3. Software Studies Initiative - 2007 NEH Office for Digital Humanities - 2008 NEH Humanities High Performance Computing - 2008 NEH/NSF Digging Into Data competition - 2009 Computational Social Science - 2009 Culturnomics and Google n-gram viewer - 2010 New York Times: “The next big idea in language, history and the arts? Data.”- 2010 softwarestudies.com 3
  • 4. How can we take advantage of unprecedented amounts of cultural data available on the web and digitized cultural heritage to begin analyzing cultural processes in new ways? How does computational analysis of the massive cultural datasets and real-time flows can help us to develop theories and methods in humanities adequate for the scale and speed of the 21st century global networked digital culture ? softwarestudies.com 4
  • 5. NEH/NSF Digging into Data competition (2009): “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?” softwarestudies.com 5
  • 6. Why study big cultural data ? softwarestudies.com 6
  • 7. 1 study societies through the social media traces (social computing) 2 more inclusive understanding of cultural history and present (using much larger samples) 3 detect large scale cultural patterns softwarestudies.com 7
  • 8. 4 generate multiple maps of the same cultural data sets (multiple “landscapes”) 5 the best way to follow global professionally produced digital culture; understand new developed cultural fields (“X” design) 6 map cultural variability and diversity softwarestudies.com 8
  • 10. Example - graph from Ted Underwood, “The Differentiation of Literary and nonliterary diction, 1700-1900.” Data: 3,724 18th century volumes, using 10,000 most frequent words (excluding proper nouns). softwarestudies.com 10
  • 11. modern (19th-20th centuries) social and cultural theory: describe what is similar (classes, structures, types) / statistics (reduction) computational humanities and social science should focus on describing what is different / variability / diversity “from data to knowledge” is wrong. In the study of culture, we need to go from our (incomplete, biased) knowledge to actual cultural data softwarestudies.com 11
  • 12. “We are no longer interested in the conformity of an individual to an ideal type; we are now interested in the relation of an individual to the other individuals with which it interacts... Relations will be more important than categories; functions, which are variable, will be more important than purposes; transitions will be more important than boundaries; sequences will be more important than hierarchies.” Louis Menand on Darvin, 2001. softwarestudies.com 12
  • 13. Visualization: Thinking without “large” categories softwarestudies.com 13
  • 14. Manual De Landa: “The ontological status of assemblages, large and small, is always that of unique, singular individuals.” “Unlike taxonomic essentialism in which genus, species and individuals are separate ontological categories, the ontology of assemblages is flat since it contains nothing but differently scaled individual singularities.” source: A New Philosophy of Society. softwarestudies.com 14
  • 15. Bruno Latour: “The ‘whole is now nothing more than a provisional visualization which can be modified and reversed at will, by moving back to the individual components, and then looking for yet other tools to regroup the same elements into alternative assemblages.” source: “Tarde’s idea of quantification.” In The Social After Gabriel Tarde: Debates and Assessments. softwarestudies.com 15
  • 16. How to study big cultural visual data in practice? How to explore massive visual collections (exploratory media analysis)? Which data analysis and visualization techniques are appropriate for non-technical users? How to democratize data analysis? softwarestudies.com 16
  • 17. Our methodology: media visualization display complete collection sorted using metadata and/or extracted features softwarestudies.com 17
  • 18. infovis: data into pictures mediavis: pictures into pictures softwarestudies.com 18
  • 19. left: scatter plot right: media visualization (image plot) of the same data softwarestudies.com 19
  • 20. our media visualization software on 287 megapixel display (image: 1 million manga pages)
  • 21. our media visualization software on newer display wall with thin bezels data: 4535 Time magazine covers) softwarestudies.com 21
  • 22. mediavis - related research: M. Worring, G.P. Nguyen. Interactive access to large image collections using similarity-based visualization. Journal of Visual Languages and Computing 19 (2008) (submitted 2005). Gerald Schaefer. Interactive Browsing of Image Repositories. ICVG 2012. Jing et al., Google Inc. Google Image Swirl: A Large-Scale Content-Based Image Visualization System. WWW 2012. softwarestudies.com 22
  • 23. mediavis vs. normal computer science approach: borrow techniques from media art, digital art, information visualization / for non-technical users explore the possibilities of simplest techniques by using them with media collections from every area of humanities use mediavis to challenge existing concepts and assumptions of humanities softwarestudies.com 23
  • 24.
  • 25. Basic media visualization techniques: 1 montage: sort images using metadata 2 slice: sample images and arrange using metadata 3 image plot: automatically measure image properties (features) and organize in 2D using these measurements and metadata softwarestudies.com 25
  • 26. 1 montage: sort images using metadata 4535 Time covers, 1923-2009 softwarestudies.com 26
  • 27. 1 montage close up: Time magazine covers, 1920s softwarestudies.com 27
  • 28. 1 montage close up: Time magazine covers, 1990s-2000s softwarestudies.com 28
  • 29. 2 slice: sample images and arrange using metadata 4535 Time covers, 1923-2009. Each line is a vertical slice through the center of an image. softwarestudies.com 29
  • 30. Time coves slice close-up softwarestudies.com 30
  • 31. 3 image plot: organize images using features and (optionally) metadata Image plots of 4535 Time covers, 1923-2009. X-axis = date; Y-axis = saturation mean. softwarestudies.com 31
  • 32. Time covers image plot close-up softwarestudies.com 32
  • 33. Comparing a number of image sets with image plots Selected paintings by six impressionist artists. X-axis = mean saturation. Y-axis = median hue. Megan O’Rourke, 2012. softwarestudies.com 33
  • 35. visualizing video collections: use media visualization with a set of keyframes automatic selection of key frames (for example, using free shot detection software) softwarestudies.com 35
  • 36. Kingdom Hearts video game 62.5 hr. of game play, 29 sessions over 20 days.ys. montage: 1 frame per 3 sec (22500 frames in total) softwarestudies.com
  • 39. 11th Year (Dziga Vertov, 1928): first frame of every shot softwarestudies.com
  • 40. 11th Year (Dziga Vertov, 1928): comparing first and last frame in every shot (close-ups from the larger visualization) softwarestudies.com 40
  • 41. Why use numbers? Using numbers to describe cultural artifacts allows to replacing discrete categories (words) with continuos descriptions (curves) softwarestudies.com 41
  • 42. 1 from timelines to graphs 2 better represent analog attributes of cultural artifacts 3 map cultural landscapes (fuzzy / overlapping / hard clusters?) 4 visualize cultural variability 5 discover new gropings softwarestudies.com 42
  • 43. 1 from timelines to curves Mark Rothko, 393 paintings (1927-1970). X - year. Y - brightness mean. Hao Wang and Mayra Vasquez. softwarestudies.com
  • 44. 2 better represent analog attributes of cultural artifacts Next slide: close-up of a visualization showing average amount of visual change (bar graph) in every shot in Vertov’s 11th year. Images above the bar: first frame of every shot. To measure visual change per shot: 1) calculate brightness mean of the difference image between each two frames in the shot 2) add all means 3) divide by number of frames in the shot softwarestudies.com
  • 46. 3 the maps of cultural landscapes reveal fuzzy and overlapping clusters - rather than discrete categories with hard boundaries softwarestudies.com 46
  • 47. 4 visualize the space of variations 600 variations of Google Logo, 1988-2009 softwarestudies.com
  • 49. Studying large massive data sets challenges our existing theoretical concepts and assumptions example: what is “style”? softwarestudies.com 49
  • 50. image plot of one million manga pages x - standard deviation y - entropy softwarestudies.com
  • 52. distribution of million manga pages x - standard deviation y - entropy softwarestudies.com 52
  • 53. single short manga series < 1000 pages softwarestudies.com 53
  • 54. 776 Vincent van Gogh paintings. X - year/month. Y - brightness mean. softwarestudies.com 54
  • 55. Current / recent projects at softwarestudies.com: 6000+ paintings of French Impressionists 7000 year old stone arrowheads (with UCSD anthropologist) softwarestudies.com 55
  • 56. samples from 4.7 million newspaper pages collection from Library of Congress (UCSD undergraduate students) virtual world / game analytics (funded by NSF Eager, with UCSD Experimental Games Lab) comparing Art Now & Graphic design Flickr groups (340,000 images) (with CS collaborator from Laurence Berkeley National Laboratory) softwarestudies.com 56
  • 57. Big project supported by Mellon Foundation Grant, 2012-2015 - tools and workflows for working with image and video collections using SEASR / MEANDRE digital humanities workflow platform - applications: 1) 1+ million images + millions of metadata records from deviantArt (the largest social network for user-created art - 20 M users, 240 M artworks). 2) 1+ million manga pages. 3) thousands of hours TV poltical news and online video softwarestudies.com 57
  • 58. Postscript: digital humanities (working with digitized collections of historical artifacts) vs. computational humanities (using social web data) softwarestudies.com 58
  • 59. “The capacity to collect and analyze massive amounts of data has transformed such fields as biology and physics. But the emergence of a data-driven 'computational social science' has been much slower. Leading journals in economics, sociology, and political science show little evidence of this field. But computational social science is occurring in Internet companies such as Google and Yahoo, and in government agencies such as the U.S. National Security Agency.” “Computational Social Science.” Science, vol. 323, no. 6, February 2009. softwarestudies.com 59
  • 60. Massive amounts of cultural content and online conversations, opinions, and cultural activities (general and specialized social media networks; personal and professional web sites ). This data offers us unprecedented opportunities to understand cultural processes and their dynamics and develop new concepts and models which can be also used to better understand the past. Currently only analyzed by Google, Facebook, YouTube, Bluefin labs, Echonest, and other companies, and computer scientists working in “social computing”- not yet by humanists. softwarestudies.com 60
  • 62. Our free open source software tools for analyzing and visualizing large image and video collections, publications and projects: softwarestudies.com The tools run on Mac, PC, Unix. All media visualizations in this presentation were created by members of Software softwarestudies.com 62