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How and why study big cultural data v2


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How and why study big cultural data v2

  1. 1. How and why study big visual cultural data Dr. Lev Manovich Professor, CUNY Graduate Center Fall 2012 version 1
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  3. 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 3
  4. 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 ? 4
  5. 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?” 5
  6. 6. Why study big cultural data ? 6
  7. 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 7
  8. 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 8
  9. 9. 9
  10. 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). 10
  11. 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 11
  12. 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. 12
  13. 13. Visualization: Thinking without “large” categories 13
  14. 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. 14
  15. 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. 15
  16. 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? 16
  17. 17. Our methodology: media visualization display complete collection sorted using metadata and/or extracted features 17
  18. 18. infovis: data into pictures mediavis: pictures into pictures 18
  19. 19. left: scatter plot right: media visualization (image plot) of the same data 19
  20. 20. our media visualization software on 287 megapixel display (image: 1 million manga pages)
  21. 21. our media visualization software on newer display wall with thin bezels data: 4535 Time magazine covers) 21
  22. 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. 22
  23. 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 23
  24. 24. 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 25
  25. 25. 1 montage: sort images using metadata 4535 Time covers, 1923-2009 26
  26. 26. 1 montage close up: Time magazine covers, 1920s 27
  27. 27. 1 montage close up: Time magazine covers, 1990s-2000s 28
  28. 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. 29
  29. 29. Time coves slice close-up 30
  30. 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. 31
  31. 31. Time covers image plot close-up 32
  32. 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. 33
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  34. 34. visualizing video collections: use media visualization with a set of keyframes automatic selection of key frames (for example, using free shot detection software) 35
  35. 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)
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  37. 37. 38
  38. 38. 11th Year (Dziga Vertov, 1928): first frame of every shot
  39. 39. 11th Year (Dziga Vertov, 1928): comparing first and last frame in every shot (close-ups from the larger visualization) 40
  40. 40. Why use numbers? Using numbers to describe cultural artifacts allows to replacing discrete categories (words) with continuos descriptions (curves) 41
  41. 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 42
  42. 42. 1 from timelines to curves Mark Rothko, 393 paintings (1927-1970). X - year. Y - brightness mean. Hao Wang and Mayra Vasquez.
  43. 43. 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
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  45. 45. 3 the maps of cultural landscapes reveal fuzzy and overlapping clusters - rather than discrete categories with hard boundaries 46
  46. 46. 4 visualize the space of variations 600 variations of Google Logo, 1988-2009
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  48. 48. Studying large massive data sets challenges our existing theoretical concepts and assumptions example: what is “style”? 49
  49. 49. image plot of one million manga pages x - standard deviation y - entropy
  50. 50. 51
  51. 51. distribution of million manga pages x - standard deviation y - entropy 52
  52. 52. single short manga series < 1000 pages 53
  53. 53. 776 Vincent van Gogh paintings. X - year/month. Y - brightness mean. 54
  54. 54. Current / recent projects at 6000+ paintings of French Impressionists 7000 year old stone arrowheads (with UCSD anthropologist) 55
  55. 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) 56
  56. 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 57
  57. 57. Postscript: digital humanities (working with digitized collections of historical artifacts) vs. computational humanities (using social web data) 58
  58. 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. 59
  59. 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. 60
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  61. 61. Our free open source software tools for analyzing and visualizing large image and video collections, publications and projects: The tools run on Mac, PC, Unix. All media visualizations in this presentation were created by members of Software 62