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Escaping greatdivide coimbra

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A long conference and a workshop that I gave (with Paul Girard) at the University of Coimbra in the framework of the project "The Importance of Being Digital". The theme of the conference was how digital methods help overcome several classic binary oppositions of traditional social sciences.

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Escaping greatdivide coimbra

  1. 1. Escaping the Great Divide How actor-network theory, digital methods and network analysis can make us sensitive to the differences in the density of associations Tommaso Venturini
  2. 2. Today’s special menu 1. Beyond the intensive / extensive discontinuity 2. Beyond the aggregating / situating discontinuity 3. Beyond the micro / macro discontinuity 4. Feeling the density of association 5. Visual Network Analysis 6. The médialab’s toolbox
  3. 3. Follow the White Rabbit why controversy mapping (and digital methods) will change everything you know about sociology Tommaso Venturini tommaso.venturini@sciences-po.fr The strabismus of social sciences Photo credit – tarout_sun via Flickr - ©
  4. 4. 3 discontinuities • 1. In data: intensive data / extensive data • 2. In methods: situating / aggregating • 3. In theory: micro-interactions / macro-structure
  5. 5. Part I Data: intensive / extensive
  6. 6. The quali/quantitative divide poor data on large population extensive data intensive data rich data on small population
  7. 7. The media as an object of study Photo credit – Brandon Doran via Flickr - ©
  8. 8. The media as carbon paper Chris Harrison Internet connections
  9. 9. The rise of digital methods Virtual reality Late ‘80-early ‘90 (Barlow, Turkle, Negroponte, Rheingold) Virtual society? 1997-2002 (Steve Woolgar et al.) Cultural analytics 2007 (Lev Manovitch) Digital methods 2009 (Richard Rogers) https://soundcloud.com/mit-cmsw/richard- rogers-digital-methods
  10. 10. Extensive data Paul Butler, 2010 Visualizing Friendships
  11. 11. Intensive data AOL user 711391 search history www.minimovies.org/documentaires/view/ilovealaska
  12. 12. Extensive and intensive data Google Flu www.google.org/flutrends
  13. 13. Extensive and intensive data Google Flu www.google.org/flutrends
  14. 14. Extensive and intensive data Google Flu www.google.org/flutrends
  15. 15. Beware! 1. Google is not the world 2. More data means more noise 3. Digital data is not your data
  16. 16. It takes more than Google to map a controversy 1. search engines are not the web 2. the web is not the Internet 3. the Internet is not the digital 4. the digital is not the world
  17. 17. Beware: more data means more noise!
  18. 18. Taking “data mining” seriously Yanacocha Gold Mine, Cajamarca, Peru
  19. 19. Compulsive hoarding
  20. 20. An (pseudo-) exhaustive map of the Web http://internet-map.net
  21. 21. A good map of the Web politicosphere.blog.lemonde.fr
  22. 22. A good map of the Web politicosphere.blog.lemonde.fr
  23. 23. Beware: digital data is not your data!
  24. 24. This is a world where massive amounts of data and applied mathematics replace every other tool that might be brought to bear. Out with every theory of human behavior, from linguistics to sociology. Forget taxonomy, ontology, and psychology. Who knows why people do what they do? The point is they do it, and we can track and measure it with unprecedented fidelity. With enough data, the numbers speak for themselves. Chris Anderson http://www.wired.com/science/discoveries/ magazine/16-07/pb_theory The end of theory?
  25. 25. Beware: more data means more noise! Askitas, N., & Zimmermann, K. (2011). Health and Well-Being in the Crisis. IZA Discussion Paper
  26. 26. Beware: more data means more noise!
  27. 27. http://googlesystem.blogspot.fr/2008/08/go ogle-suggest-enabled-by-default.html Beware: more data means more noise!
  28. 28. Part II Methods: situating / aggregating
  29. 29. (Collective) life is complicated Andreas Gursky 1999 Chicago, Board of Trade II
  30. 30. Situating VS aggregating
  31. 31. La fabrique de la loi http://www.lafabriquedelaloi.fr
  32. 32. http://contropedia.net/demo Contropedia
  33. 33. Borra, E., Weltevrede, E., Ciuccarelli, P., Kaltenbrunner, A., Laniado, D., Magni, G., Mauri, M., Rogers, R. and Venturini, T. (2014). Contropedia - the analysis and visualization of controversies in Wikipedia articles. In OpenSym ’14: The International Symposium on Open Collaboration Proceedings. http://contropedia.net/demo Contropedia
  34. 34. http://www.climaps.eu EMAPS (climaps.eu)
  35. 35. 2014 - Venturini, T., Baya-laffite, N., Cointet, J., Gray, I., Zabban, V., & De Pryck, K. Three Maps and Three Misunderstandings: A Digital Mapping of Climate Diplomacy. Big Data & Society, 1:1 EMAPS (climaps.eu) http://www.climaps.eu
  36. 36. Part III Theory: micro-interactions / macro-structure
  37. 37. The micro/macro distinction Merian & Jonston 1718 Folio Ants, Clony, Nest, Insects Thomas Hobbes, 1651 The Leviathan
  38. 38. What micro/macro means An ontological fracture The collective self is not a simple epiphenomenon of its morphologic base, precisely as the individual self is not a simple efflorescence of the nervous system. For the collective self to appear, a sui generis synthesis of individual self has to be produced. This synthesis creates a world of feelings, ideas, images that, once come to life, follow their own laws. An emergent fracture In certain historical periods, social interactions become much more frequent and active. Individuals seek one another out and come together more. The result is the general effervescence that is characteristic of revolutionary or creative epochs… This stimulating action of society is not felt in exceptional circumstances alone. There is virtually no instant of our lives in which a certain rush of energy fails to come to us from outside ourselves. Emile Durkheim, 1912 Le formes élémentaires de la vie religieuse
  39. 39. What micro/macro hides http://zgrossbart.github.io/hborecycling/ the ontological fracture hides other (more relevant) fractures
  40. 40. What micro/macro hides http://zgrossbart.github.io/hborecycling/ the ontological fracture hides other (more relevant) fractures The emergent fracture hides the work to build and maintain it http://en.wikipedia.org/wiki/Maxwell's_demon
  41. 41. What is disorder I am personally rather tolerant of disorder. But I always remember how unrelaxed I felt in a particular bathroom which was kept spotlessly clean in so far as the removal of grime and grease was concerned. It had been installed in an old house in a space created by the simple expedient of setting a door at each end of a corridor between two staircases. The decor remained unchanged: the engraved portrait of Vinogradoff, the books, the gardening tools, the row of gumboots. It all made good sense as the scene of a back corridor, but as a bathroom – the impression destroyed repose. Mary Douglas (1966) Purity and Danger
  42. 42. What is disorder In chasing dirt, in papering, decorating, tidying we are not governed by anxiety to escape disease, but are positively re-ordering our environment, making it conform to an idea. There is nothing fearful or unreasoning in our dirt- avoidance: it is a creative movement, an attempt to relate form to function, to make unity of experience. If this is so with our separating, tidying and purifying, we should interpret primitive purification and prophylaxis in the same light. Mary Douglas (1966) Purity and Danger
  43. 43. From boundaries to boundary work Fences make good neighbors Gieryn, Thomas F. (1983) Boundary-work the demarcation of science from non-science American Sociological Review 48(6): 781–795 Demarcation is as much a practical problem for scientists as an analytical problem for sociologists and philosophers
  44. 44. The lesson of ANT It is not that in collective life there are no boundaries (between micro and macro, science and politics…) It is that all boundaries are constantly constructed, de-constructed and re-constructed Social researchers cannot take social boundaries for granted, for their job is to study such work of (de-/re-)construction (Venturini, T. (2010). Diving in magma: how to explore controversies with actor-network theory. In Public Understanding of Science, 19(3), 258–273. )
  45. 45. In the Presence of the Holy See UNRWA photo archive image of Dheisheh refugee camp after the 1948 partition justaposed with T. Habjouqa’s 2012 photo of Israel’s wall near Beit Hanina, Jerusalem.
  46. 46. Part IV Becoming sensitive to the differences in the density of association
  47. 47. 3 discontinuities • 1. In data: intensive data / extensive data • 2. In methods: situating / aggregating • 3. In theory: micro-interactions / macro-structure
  48. 48. Overcoming the 3 discontinuities • 1. In data: intensive data / extensive data Digital traceability and computation (data scientists) • 2. In methods: situating / aggregating Datascape navigation (designers) • 3. In theory: micro-interactions / macro-structure A non-emergentist theory of action (actor-network theorist)
  49. 49. The fabric of (cooked) rice Roland Barthes (1970) The Empire of Signs Cooked rice (whose absolutely special identity is attested by a special name, which is not that of raw rice) can be defined only by a contradiction of substance; it is at once cohesive and detachable; its substantial destination is the fragment, the clump; the volatile conglomerate… it constitutes in the picture a compact whiteness, granular (contrary to that of our bread) and yet friable: what comes to the table to the table, dense and stuck together, comes undone at a touch of the chopsticks, though without ever scattering, as if division occurred only to produce still another irreducible cohesion (pp. 12-14).
  50. 50. Why are we so fascinated by networks? Paul Butler, 2010 Visualizing Friendships
  51. 51. A network (graph) is not a network (actor-network) Actor-Network Theory Complex Network Analysis Actors and networks have the same properties (they are the same) ≠ Networks are composite while nodes are indivisible and uncombinable Different mediations (can) have different effects ≠ All edges have the same effect (possibly with different weight) Different actors (can) have different association potential ≠ All nodes have equal linking potential A-N are always seen from one or more specific viewpoints ≠ Networks are usually seen from above/outside What counts is change ≠ Networks are statics
  52. 52. A network (graph) is not a network (actor-network)
  53. 53. A question of resonance A diagram of a network, then, does not look like a network but maintain the same qualities of relations – proximities, degrees of separation, and so forth – that a network also requires in order to form. Resemblance should here be considered a resonating rather than a hierarchy (a form) that arranges signifiers and signified within a sign (p. 24). Munster, A. (2013). An Aesthesia of Networks Cambridge Mass.: MIT Press
  54. 54. Networks
  55. 55. Mathematical networks analysis Euler, 1736, Solutio problematis ad geometriam situs pertinentis
  56. 56. Visual networks analysis
  57. 57. The fabric of collective life Jacob L. Moreno, April 3, 1933 The New York Times Social life is continuous but not homogenous Doing social research is becoming sensitive to the differences in the density of association
  58. 58. Network as maps London Underground 1920 map homepage.ntlworld.com/clivebillson/tube/tube.html - www.fourthway.co.uk/tfl.html
  59. 59. Network as maps London Underground 1933 map (Harry Beck) homepage.ntlworld.com/clivebillson/tube/tube.html - www.fourthway.co.uk/tfl.html
  60. 60. Force-vector algorithms
  61. 61. Force-vectors’ magic trick
  62. 62. Force-vectors’ magic trick Jacomy, M., Venturini, T., Heyman, S. & Bastian, M. (2014) ForceAtlas2, a Continuous Graph Layout Algorithm for Handy Network Visualization Designed for the Gephi Software. PlosONE, 9:6
  63. 63. Force-vector: Yes, but which?
  64. 64. Can we trust force-vectors? NoYes
  65. 65. Can we trust force-vectors? NoYes ?!?
  66. 66. Part V Visual Network Analysis
  67. 67. Semiology of graphics Bertin J., Sémiologie graphique, Paris, Mouton/Gauthier-Villars, 1967
  68. 68. Visual (aka preattentive) variables
  69. 69. Visual variables A B C
  70. 70. A. nodes position – layout B. nodes size – ranking C. nodes color – partitions 3 visual variables of analysis Gephi.org
  71. 71. Visual network analysis questions A. Position (force-vector spatialization) 1. Nodes density Where are structural holes (under-populated regions)? Where are clusters an sub-clusters (over-populated regions)? Which are the largest and most cohesive clusters? 2. Relative position Which nodes/clusters are globally and locally central? Which nodes/clusters are global and local bridges (between clusters)? B. Size (ranking by in-degree / out-degree) 3. Nodes connectivity Which nodes are the authorities (receive most connections)? Which nodes are the hub (originate most connections)? C. Color (color by partition) 4. Distribution Is typology coherent with topology (partitions coincide with clusters)? Which are the exceptions (‘misplaced nodes’)?
  72. 72. Technical step: Spatialization with ForceAtlas 2 • LinLog mode (maximizes the legibility of clusters) • Prevent overlap (enhances legibility, but distorts spatialization) • Scaling (increases/decreases all distance proportionally) • Gravity (pulls everything towards the center, prevents dispersions, but distorts spatialization) • Approximate repulsion (accelerate spatialization on large graphs, but distorts spatialization)
  73. 73. Visual network analysis questions A. Position (force-vector spatialization) 1. Nodes density Where are structural holes (under-populated regions)? Where are clusters an sub-clusters (over-populated regions)? Which are the largest and most cohesive clusters? 2. Relative position Which nodes/clusters are globally and locally central? Which nodes/clusters are global and local bridges (between clusters)? B. Size (ranking by in-degree / out-degree) 3. Nodes connectivity Which nodes are the authorities (receive most connections)? Which nodes are the hub (originate most connections)? C. Color (color by partition) 4. Distribution Is typology coherent with topology (partitions coincide with clusters)? Which are the exceptions (‘misplaced nodes’)?
  74. 74. Reading principle: Identify regions where the density of nodes is - lower (structural holes) - higher (clusters) Questions: - Where are structural holes? - Where are clusters an sub-clusters? - Which clusters are most represented in the network? - Which clusters are most cohesive? A.1. Position: nodes density
  75. 75. Main cluster and structural holes
  76. 76. Sub-clusters
  77. 77. Modularity
  78. 78. Denser and larger clusters
  79. 79. Visual network analysis questions A. Position (force-vector spatialization) 1. Nodes density Where are structural holes (under-populated regions)? Where are clusters an sub-clusters (over-populated regions)? Which are the largest and most cohesive clusters? 2. Relative position Which nodes/clusters are globally and locally central? Which nodes/clusters are global and local bridges (between clusters)? B. Size (ranking by in-degree / out-degree) 3. Nodes connectivity Which nodes are the authorities (receive most connections)? Which nodes are the hub (originate most connections)? C. Color (color by partition) 4. Distribution Is typology coherent with topology (partitions coincide with clusters)? Which are the exceptions (‘misplaced nodes’)?
  80. 80. Reading principle: Indentify what is in the center - of the graph - of each cluster Identify what is between clusters Questions: - Which nodes/clusters are globally and locally central? - Which nodes/clusters are global and local bridges? A.2. Position: relative position
  81. 81. Central nodes and clusters
  82. 82. Bridging nodes and clusters
  83. 83. Technical step: The ranking palette
  84. 84. Visual network analysis questions A. Position (force-vector spatialization) 1. Nodes density Where are structural holes (under-populated regions)? Where are clusters an sub-clusters (over-populated regions)? Which are the largest and most cohesive clusters? 2. Relative position Which nodes/clusters are globally and locally central? Which nodes/clusters are global and local bridges (between clusters)? B. Size (ranking by in-degree / out-degree) 3. Nodes connectivity Which nodes are the authorities (receive most connections)? Which nodes are the hub (originate most connections)? C. Color (color by partition) 4. Distribution Is typology coherent with topology (partitions coincide with clusters)? Which are the exceptions (‘misplaced nodes’)?
  85. 85. Reading principle: Indentify which nodes that - receive more connections - originate more connections Questions: Which are the authorities of the network? Which are the hubs of the network? B.3. Size: node connectivity
  86. 86. Authorities
  87. 87. Hubs
  88. 88. Technical step: Data laboratory window Gephi.org
  89. 89. Technical step: the partition palette
  90. 90. Visual network analysis questions A. Position (force-vector spatialization) 1. Nodes density Where are structural holes (under-populated regions)? Where are clusters an sub-clusters (over-populated regions)? Which are the largest and most cohesive clusters? 2. Relative position Which nodes/clusters are globally and locally central? Which nodes/clusters are global and local bridges (between clusters)? B. Size (ranking by in-degree / out-degree) 3. Nodes connectivity Which nodes are the authorities (receive most connections)? Which nodes are the hub (originate most connections)? C. Color (color by partition) 4. Distribution Is typology coherent with topology (partitions coincide with clusters)? Which are the exceptions (‘misplaced nodes’)?
  91. 91. Reading principle: - Evaluate if nodes of the same color are close - Identify ‘misplaced’ nodes Questions: - Is typology coherent with topology? - Which are the exceptions? C.4. Color: distribution
  92. 92. Typology and topology
  93. 93. Exceptions
  94. 94. Polarization
  95. 95. Polarization
  96. 96. Visual network analysis
  97. 97. Visual network analysis Venturini, T., Jacomy, M, De Carvalho Pereira, D. Visual Network Analysis: The example of the rio+20 online debate (working paper)
  98. 98. Part VI The médialab toolbox
  99. 99. The médialab toolkit http://tools.medialab.sciences-po.fr
  100. 100. The médialab toolkit https://github.com/medialab
  101. 101. The médialab toolkit
  102. 102. The médialab toolkit
  103. 103. Sciencescape http://tools.medialab.sciences- po.fr/sciencescape/
  104. 104. Sciencescape http://tools.medialab.sciences- po.fr/sciencescape/ Sciencescape is a simple, client-side, javascript tool intended to extract • time-curves • sankey-diagrams • co-occurrence networks from bibliographical notices exported from • ISI Web of Science • Scopus
  105. 105. Sciencescape Journal over time (ANT from Scopus)
  106. 106. Sciencescape Keyword over time (ANT from Scopus)
  107. 107. Sciencescape Authors-Keywords-Journals sankey (ANT from Scopus)
  108. 108. Sciencescape Keywords’ network (ANT from Scopus)
  109. 109. Sciencescape Future developments
  110. 110. Table2Net http://tools.medialab.sciences- po.fr/table2net/
  111. 111. Table2Net http://tools.medialab.sciences- po.fr/table2net/ Table2Net is a generic, client-side, javascript tool intended to extract (Gephi) networks from any data-table The tool is able to produce • mono-partite and bi-partite networks • weighted and non-weighted networks • static and dynamic networks
  112. 112. Table2Net http://tools.medialab.sciences- po.fr/table2net/ Normal Bipartite
  113. 113. Hyphe http://hyphe.medialab.sciences-po.fr/demo/
  114. 114. Hyphe http://hyphe.medialab.sciences-po.fr/demo/ Hyphe is a powerful, server-side tool intended to assist scholars in the building of web corpus Compared to previous tools (issuecrawler, navicrawler) • it allows a more flexible definition of ‘web-entities’ • it implement a semi-automatic semi-manual crawling
  115. 115. Hyphe Flexible definition of ‘web-entities’
  116. 116. Hyphe Flexible definition of ‘web-entities’
  117. 117. Hyphe Semi-automatic semi-manual crawling
  118. 118. Hyphe Semi-automatic semi-manual crawling
  119. 119. Hyphe The future interface (under construction)
  120. 120. ANTA actor-network text analyzer http://jiminy.medialab.sciences-po.fr/anta_dev/
  121. 121. ANTA is an experimental, server-side tool intended to assist scholars in extracting networks of occurrence of noun-phrases in textual corpuses The tool allow to • create a corpus of textual documents • extract noun-phrases from the corpus (entities) • select the more relevant entities • generate a bi-partite network of documents and entities ANTA actor-network text analyzer http://jiminy.medialab.sciences-po.fr/anta_dev/
  122. 122. ANTA http://jiminy.medialab.sciences-po.fr/anta_dev/
  123. 123. ANTA actor-network text analyzer http://jiminy.medialab.sciences-po.fr/anta_dev/
  124. 124. ANTA http://jiminy.medialab.sciences-po.fr/anta_dev/
  125. 125. Venturini, T., Gemenne, F., & Severo, M. (2013). Des Migrants et des Mots. Une analyse numérique des débats médiatiques sur les migrations et l’environnement. In Cultures & Conflits, 88(4). Venturini, T., & Guido, D. (2012). Once Upon a Text : an ANT Tale in Text Analysis. In Sociologica ANTA http://jiminy.medialab.sciences-po.fr/anta_dev/
  126. 126. The médialab toolkit
  127. 127. Gephi https://gephi.github.io/
  128. 128. Gephi https://gephi.github.io/ Gephi is a powerful, stand-alone tool for network analysis Compared to other tools, Gephi • is more user-friendly • translate graph mathematics in visual variables • allows direct network manipulation
  129. 129. Heatgraph From networks to heatmaps
  130. 130. RÉFÉRENCE Turing AM, 1952, Phil. Trans. of the Royal Society of Bio. Sciences INSTITUTION specialisée Blackett Lab. Imperial College MOT CLE Magnetic properties INSTITUTION non-specialisée Ecole Polytechnique de Zurich Heatgraph Ego-centered heatgraphs
  131. 131. Heatgraph http://tools.medialab.sciences-po.fr/heatgraph/
  132. 132. Heatgraph http://tools.medialab.sciences-po.fr/heatgraph/ Severo, M. & Venturini, T. (forthcoming) Intangible Cultural Heritage Webs: Comparing national networks through digital methods. In New Media & Society
  133. 133. http://www.tommasoventurini.it/

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