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.

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. 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. 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. 3 discontinuities
• 1. In data:
intensive data / extensive data
• 2. In methods:
situating / aggregating
• 3. In theory:
micro-interactions / macro-structure

5. Part I Data:
intensive / extensive

6. The quali/quantitative divide
poor data on large population
extensive data
intensive data
rich data on small population

7. The media as an
object of study
Photo credit – Brandon Doran via Flickr - ©

8. The media as carbon
paper
Chris Harrison
Internet connections

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. Extensive data Paul Butler, 2010
Visualizing Friendships

11. Intensive data AOL user 711391 search history
www.minimovies.org/documentaires/view/ilovealaska

12. Extensive and
intensive data
Google Flu
www.google.org/flutrends

13. Extensive and
intensive data
Google Flu
www.google.org/flutrends

14. Extensive and
intensive data
Google Flu
www.google.org/flutrends

15. Beware!
1. Google is not the world
2. More data means more noise
3. Digital data is not your data

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. Beware: more data
means more noise!

18. Taking “data mining”
seriously Yanacocha Gold Mine,
Cajamarca, Peru

19. Compulsive hoarding

20. An (pseudo-) exhaustive
map of the Web http://internet-map.net

21. A good
map of the Web politicosphere.blog.lemonde.fr

22. A good
map of the Web politicosphere.blog.lemonde.fr

23. Beware: digital data
is not your data!

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. Beware: more data means
more noise!
Askitas, N., & Zimmermann, K. (2011).
Health and Well-Being in the Crisis. IZA
Discussion Paper

26. Beware: more data means
more noise!

27. http://googlesystem.blogspot.fr/2008/08/go
ogle-suggest-enabled-by-default.html
Beware: more data means
more noise!

28. Part II Methods:
situating / aggregating

29. (Collective) life
is complicated Andreas Gursky 1999
Chicago, Board of Trade II

30. Situating VS aggregating

31. La fabrique de la loi
http://www.lafabriquedelaloi.fr

32. http://contropedia.net/demo
Contropedia

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. http://www.climaps.eu
EMAPS (climaps.eu)

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. Part III Theory:
micro-interactions /
macro-structure

37. The micro/macro distinction
Merian & Jonston 1718 Folio
Ants, Clony, Nest, Insects
Thomas Hobbes, 1651
The Leviathan

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. What micro/macro hides
http://zgrossbart.github.io/hborecycling/
the ontological fracture hides
other (more relevant) fractures

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. 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. 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. 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. 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. 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. Part IV Becoming sensitive to the
differences in the density of
association

47. 3 discontinuities
• 1. In data:
intensive data / extensive data
• 2. In methods:
situating / aggregating
• 3. In theory:
micro-interactions / macro-structure

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. 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. Why are we so fascinated by
networks?
Paul Butler, 2010
Visualizing Friendships

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. A network (graph)
is not a network (actor-network)

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. Networks

55. Mathematical networks
analysis
Euler, 1736, Solutio problematis ad
geometriam situs pertinentis

56. Visual
networks analysis

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. Network as maps London Underground
1920 map
homepage.ntlworld.com/clivebillson/tube/tube.html - www.fourthway.co.uk/tfl.html

59. Network as maps London Underground
1933 map (Harry Beck)
homepage.ntlworld.com/clivebillson/tube/tube.html - www.fourthway.co.uk/tfl.html

60. Force-vector algorithms

61. Force-vectors’ magic trick

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. Force-vector:
Yes, but which?

64. Can we trust force-vectors?
NoYes

65. Can we trust force-vectors?
NoYes ?!?

66. Part V Visual Network Analysis

67. Semiology
of graphics Bertin J., Sémiologie graphique, Paris,
Mouton/Gauthier-Villars, 1967

68. Visual (aka preattentive) variables

69. Visual variables
A B
C

70. A. nodes position – layout
B. nodes size – ranking
C. nodes color – partitions
3 visual variables of analysis
Gephi.org

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. 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. 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. 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. Main cluster and structural holes

76. Sub-clusters

77. Modularity

78. Denser and larger clusters

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. 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. Central nodes and clusters

82. Bridging nodes and clusters

83. Technical step:
The ranking palette

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. 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. Authorities

87. Hubs

88. Technical step:
Data laboratory window Gephi.org

89. Technical step:
the partition palette

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. 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. Typology and topology

93. Exceptions

94. Polarization

95. Polarization

96. Visual network analysis

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. Part VI The médialab toolbox

99. The médialab
toolkit http://tools.medialab.sciences-po.fr

100. The médialab
toolkit https://github.com/medialab

101. The médialab toolkit

102. The médialab toolkit

103. Sciencescape http://tools.medialab.sciences-
po.fr/sciencescape/

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. Sciencescape Journal over time
(ANT from Scopus)

106. Sciencescape Keyword over time
(ANT from Scopus)

107. Sciencescape Authors-Keywords-Journals sankey
(ANT from Scopus)

108. Sciencescape Keywords’ network
(ANT from Scopus)

109. Sciencescape Future developments

110. Table2Net http://tools.medialab.sciences-
po.fr/table2net/

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. Table2Net http://tools.medialab.sciences-
po.fr/table2net/
Normal
Bipartite

113. Hyphe http://hyphe.medialab.sciences-po.fr/demo/

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. Hyphe Flexible definition of ‘web-entities’

116. Hyphe Flexible definition of ‘web-entities’

117. Hyphe Semi-automatic semi-manual crawling

118. Hyphe Semi-automatic semi-manual crawling

119. Hyphe The future interface
(under construction)

120. ANTA
actor-network
text analyzer http://jiminy.medialab.sciences-po.fr/anta_dev/

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. ANTA http://jiminy.medialab.sciences-po.fr/anta_dev/

123. ANTA
actor-network
text analyzer http://jiminy.medialab.sciences-po.fr/anta_dev/

124. ANTA http://jiminy.medialab.sciences-po.fr/anta_dev/

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. The médialab toolkit

127. Gephi https://gephi.github.io/

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. Heatgraph From networks
to heatmaps

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. Heatgraph http://tools.medialab.sciences-po.fr/heatgraph/

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. http://www.tommasoventurini.it/