How and why study big cultural data v2

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Visualizing large image and video collections: techniques, examples, theory.

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How and why study big cultural data v2

  1. 1. How and why study bigvisual cultural dataDr. Lev ManovichProfessor, CUNY Graduate Centermanovich.lev@gmail.comsoftwarestudies.comFall 2012 version 1
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  3. 3. Software Studies Initiative - 2007NEH Office for Digital Humanities - 2008NEH Humanities High Performance Computing - 2008NEH/NSF Digging Into Data competition - 2009Computational Social Science - 2009Culturnomics and Google n-gram viewer - 2010New York Times: “The next big idea in language,history and the arts? Data.”- 2010 3
  4. 4. How can we take advantage of unprecedentedamounts of cultural data available on the weband digitized cultural heritage to begin analyzingcultural processes in new ways?How does computational analysis of themassive cultural datasets and real-time flowscan help us to develop theories and methods inhumanities adequate for the scale and speed ofthe 21st century global networked digitalculture ? 4
  5. 5. NEH/NSF Digging into Data competition (2009):“How does the notion of scale affecthumanities and social science research?Now that scholars have access to hugerepositories of digitized data—far more thanthey could read in a lifetime—what does thatmean for research?” 5
  6. 6. Why studybig cultural data ? 6
  7. 7. 1 study societies through the social mediatraces (social computing)2 more inclusive understanding of culturalhistory and present (using much largersamples)3 detect large scale cultural patterns 7
  8. 8. 4 generate multiple maps of the same culturaldata sets (multiple “landscapes”)5 the best way to follow global professionallyproduced digital culture; understand newdeveloped cultural fields (“X” design)6 map cultural variability and diversity 8
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  10. 10. Example - graph from Ted Underwood, “The Differentiation of Literaryand 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 andcultural theory: describe what is similar(classes, structures, types) / statistics(reduction)computational humanities and social scienceshould focus on describing what is different /variability / diversity“from data to knowledge” is wrong. In thestudy of culture, we need to go from our(incomplete, biased) knowledge to actualcultural data 11
  12. 12. “We are no longer interested in the conformityof an individual to an ideal type; we are nowinterested in the relation of an individual to theother individuals with which it interacts...Relations will be more important thancategories; functions, which are variable, willbe more important than purposes; transitionswill be more important than boundaries;sequences will be more important thanhierarchies.”Louis Menand on Darvin, 2001. 12
  13. 13. Visualization: Thinkingwithout “large” categories 13
  14. 14. Manual De Landa:“The ontological status of assemblages, largeand small, is always that of unique, singularindividuals.”“Unlike taxonomic essentialism in whichgenus, species and individuals are separateontological categories, the ontology ofassemblages is flat since it contains nothingbut differently scaled individual singularities.”source: A New Philosophy of Society. 14
  15. 15. Bruno Latour:“The ‘whole is now nothing more than aprovisional visualization which can bemodified and reversed at will, by moving backto the individual components, and thenlooking for yet other tools to regroup the sameelements into alternative assemblages.”source: “Tarde’s idea of quantification.” InThe Social After Gabriel Tarde: Debates andAssessments. 15
  16. 16. How to study big culturalvisual data in practice?How to explore massive visual collections(exploratory media analysis)?Which data analysis and visualizationtechniques are appropriate for non-technicalusers? How to democratize data analysis? 16
  17. 17. Our methodology:media visualizationdisplay completecollection sorted usingmetadata and/or extractedfeatures 17
  18. 18. infovis: data into picturesmediavis: pictures into pictures 18
  19. 19. left: scatter plotright: 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 newerdisplay wall with thin bezelsdata: 4535 Time magazine covers) 21
  22. 22. mediavis - related research:M. Worring, G.P. Nguyen. Interactive access to largeimage collections using similarity-based visualization.Journal of Visual Languages and Computing 19 (2008)(submitted 2005).Gerald Schaefer. Interactive Browsing of ImageRepositories. ICVG 2012.Jing et al., Google Inc. Google Image Swirl: A Large-ScaleContent-Based Image Visualization System. WWW 2012. 22
  23. 23. mediavis vs. normalcomputer science approach:borrow techniques from media art, digital art,information visualization / for non-technical usersexplore the possibilities of simplest techniques byusing them with media collections from every areaof humanitiesuse mediavis to challenge existing concepts andassumptions of humanities 23
  24. 24. Basic media visualizationtechniques:1 montage: sort images using metadata2 slice: sample images and arrange usingmetadata3 image plot: automatically measure imageproperties (features) and organize in 2D usingthese measurements and metadata 25
  25. 25. 1montage: sort imagesusing metadata4535 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. 2slice: sample images and arrange using metadata4535 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) metadataImage 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 plotsSelected 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 videocollections:use media visualization with a set ofkeyframesautomatic selection of key frames(for example, using free shot detectionsoftware) 35
  35. 35. Kingdom Hearts video game62.5 hr. of game play, 29 sessions over 20 days.ys.montage: 1 frame per 3 sec (22500 frames in total)
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  38. 38. 11th Year (Dziga Vertov, 1928): first frame of every shot
  39. 39. 11th Year (Dziga Vertov, 1928): comparing firstand last frame in every shot (close-ups fromthe larger visualization) 40
  40. 40. Why use numbers?Using numbers to describecultural artifacts allows toreplacing discretecategories (words) withcontinuos descriptions(curves) 41
  41. 41. 1 from timelines to graphs2 better represent analog attributesof cultural artifacts3 map cultural landscapes (fuzzy /overlapping / hard clusters?)4 visualize cultural variability5 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 artifactsNext slide:close-up of a visualization showing average amount ofvisual change (bar graph) in every shot in Vertov’s11th year. Images above the bar: first frame of everyshot.To measure visual change per shot:1) calculate brightness mean of the difference imagebetween each two frames in the shot2) add all means3) divide by number of frames in the shot
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  45. 45. 3 the maps of cultural landscapes reveal fuzzy andoverlapping clusters - rather than discrete categorieswith hard boundaries 46
  46. 46. 4 visualize the space of variations600 variations of Google Logo, 1988-2009
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  48. 48. Studying large massivedata sets challenges ourexisting theoreticalconcepts and assumptionsexample: what is “style”? 49
  49. 49. image plot of one million manga pagesx - standard deviationy - entropy
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  51. 51. distribution ofmillion manga pagesx - standard deviationy - 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 projectsat paintings of French Impressionists7000 year old stone arrowheads(with UCSD anthropologist) 55
  55. 55. samples from 4.7 million newspaper pagescollection from Library of Congress (UCSDundergraduate students)virtual world / game analytics (funded by NSFEager, with UCSD Experimental Games Lab)comparing Art Now & Graphic design Flickrgroups (340,000 images)(with CS collaborator from Laurence BerkeleyNational Laboratory) 56
  56. 56. Big project supported by Mellon FoundationGrant, 2012-2015- tools and workflows for working with imageand video collections using SEASR / MEANDREdigital humanities workflow platform- applications:1) 1+ million images + millions of metadatarecords from deviantArt (the largest socialnetwork for user-created art - 20 M users, 240 Martworks).2) 1+ million manga pages.3) thousands of hours TV poltical news andonline video 57
  57. 57. Postscript:digital humanities (workingwith digitized collections ofhistorical artifacts)vs. computational humanities(using social web data) 58
  58. 58. “The capacity to collect and analyze massive amountsof data has transformed such fields as biology andphysics. But the emergence of a data-drivencomputational social science has been much slower.Leading journals in economics, sociology, and politicalscience show little evidence of this field. Butcomputational social science is occurring in Internetcompanies such as Google and Yahoo, and ingovernment agencies such as the U.S. NationalSecurity Agency.”“Computational Social Science.” Science, vol. 323, no.6, February 2009. 59
  59. 59. Massive amounts of cultural content and onlineconversations, opinions, and cultural activities(general and specialized social media networks;personal and professional web sites ).This data offers us unprecedented opportunities tounderstand cultural processes and their dynamicsand develop new concepts and models which can bealso used to better understand the past.Currently only analyzed by Google, Facebook,YouTube, Bluefin labs, Echonest, and othercompanies, and computer scientists working in“social computing”- not yet by humanists. 60
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  61. 61. Our free open source software tools foranalyzing and visualizing large image andvideo collections, publications andprojects:softwarestudies.comThe tools run on Mac, PC, Unix.All media visualizations in this presentationwere created by members of Software 62