escaping greatdivide coimbra
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Escaping the Great DivideHow actor-network theory, digital methods and network analysis can make us sensitive to the
differences in the density of associations
Tommaso Venturini
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
Follow the White Rabbitwhy controversy mapping (and digital methods)
will change everything you know about sociology
Tommaso Venturini
tommaso.venturini@sciences-po.fr
The strabismusof social sciences
Photo credit – tarout_sun via Flickr - ©
3 discontinuities
• 1. In data:intensive data / extensive data
• 2. In methods:situating / aggregating
• 3. In theory:micro-interactions / macro-structure
Part IData:
intensive / extensive
The quali/quantitative divide
poor data on large populationextensive data
intensive datarich data on small population
The media as an object of study
Photo credit – Brandon Doran via Flickr - ©
The media as carbon paper
Chris HarrisonInternet connections
The rise ofdigital 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 methods2009 (Richard Rogers)
https://soundcloud.com/mit-cmsw/richard-rogers-digital-methods
Extensive data Paul Butler, 2010Visualizing Friendships
Intensive data AOL user 711391 search historywww.minimovies.org/documentaires/view/ilovealaska
Extensive andintensive data
Google Fluwww.google.org/flutrends
Extensive andintensive data
Google Fluwww.google.org/flutrends
Extensive andintensive data
Google Fluwww.google.org/flutrends
Beware!
1.Google is not the world
2.More data means more noise
3.Digital data is not your data
It takes more than Googleto 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
Beware: more data means more noise!
Taking “data mining” seriously
Yanacocha Gold Mine,Cajamarca, Peru
Compulsive hoarding
An (pseudo-) exhaustive map of the Web http://internet-map.net
A goodmap of the Web politicosphere.blog.lemonde.fr
A goodmap of the Web politicosphere.blog.lemonde.fr
Beware: digital datais not your data!
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 Andersonhttp://www.wired.com/science/discoveries/magazine/16-07/
pb_theory
The end of theory?
Beware: more data means more noise!
Askitas, N., & Zimmermann, K. (2011). Health and Well-Being in the Crisis. IZA Discussion
Paper
Beware: more data means more noise!
http://googlesystem.blogspot.fr/
2008/08/google-suggest-enabled-by-default.html
Beware: more data means more noise!
Part II Methods:
situating /
aggregating
(Collective) lifeis complicated Andreas Gursky 1999
Chicago, Board of Trade II
Situating VS aggregating
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
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
Part III Theory:
micro-interactions /
macro-structure
The micro/macro distinction
Merian & Jonston 1718 Folio Ants, Clony,
Nest, Insects
Thomas Hobbes, 1651The Leviathan
What micro/macro means
An ontological fractureThe 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 fractureIn 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 characteristicof 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, 1912Le formes
élémentaires de la vie religieuse
What micro/macro hides
http://zgrossbart.github.io/hborecycling/
the ontological fracture hidesother (more relevant) fractures
What micro/macro hides
http://zgrossbart.github.io/hborecycling/
the ontological fracture hidesother (more relevant) fractures
The emergent fracture hidesthe work to build and maintain it
http://en.wikipedia.org/wiki/Maxwell's_demon
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
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
From boundariesto 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
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. )
In the Presenceof the Holy See
UNRWA photo archive image of Dheisheh refugee campafter the 1948 partition justaposed with T. Habjouqa’s
2012 photo of Israel’s wall near Beit Hanina, Jerusalem.
Part IV Becoming
sensitive to the
differences in the
density of
association
3 discontinuities
• 1. In data:intensive data / extensive data
• 2. In methods:situating / aggregating
• 3. In theory:micro-interactions / macro-structure
Overcoming the3 discontinuities
• 1. In data:intensive data / extensive dataDigital traceability and computation (data scientists)
• 2. In methods:situating / aggregatingDatascape navigation (designers)
• 3. In theory:micro-interactions / macro-structureA non-emergentist theory of action (actor-network theorist)
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).
Why are we so fascinated by networks?
Paul Butler, 2010Visualizing Friendships
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
A network (graph)is not a network (actor-network)
A questionof 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
Networks
Mathematical networks analysisEuler, 1736, Solutio problematis ad
geometriam situs pertinentis
Visualnetworks analysis
The fabric ofcollective life
Jacob L. Moreno, April 3, 1933The New York Times
Social life is continuous but not homogenousDoing social research is becoming sensitive tothe differences in the density of association
Network as maps London Underground1920 map
homepage.ntlworld.com/clivebillson/tube/tube.html - www.fourthway.co.uk/tfl.html
Network as maps London Underground1933 map (Harry Beck)
homepage.ntlworld.com/clivebillson/tube/tube.html - www.fourthway.co.uk/tfl.html
Force-vector algorithms
Force-vectors’ magic trick
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
Force-vector:Yes, but which?
Can we trust force-vectors?
NoYes
Can we trust force-vectors?
NoYes ?!?
Part VVisual Network
Analysis
Semiologyof graphics Bertin J., Sémiologie graphique,
Paris, Mouton/Gauthier-Villars, 1967
Visual (aka preattentive) variables
Visual variables
A B
C
A. nodes position – layout
B. nodes size – ranking
C. nodes color – partitions
3 visual variables of analysisGephi.org
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 positionWhich 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 connectivityWhich nodes are the authorities (receive most connections)?Which nodes are the hub (originate most connections)?
C. Color (color by partition)
4. DistributionIs typology coherent with topology (partitions coincide with clusters)?Which are the exceptions (‘misplaced nodes’)?
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)
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 positionWhich 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 connectivityWhich nodes are the authorities (receive most connections)?Which nodes are the hub (originate most connections)?
C. Color (color by partition)
4. DistributionIs typology coherent with topology (partitions coincide with clusters)?Which are the exceptions (‘misplaced nodes’)?
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
Main cluster and structural holes
Sub-clusters
Modularity
Denser and larger clusters
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 positionWhich 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 connectivityWhich nodes are the authorities (receive most connections)?Which nodes are the hub (originate most connections)?
C. Color (color by partition)
4. DistributionIs typology coherent with topology (partitions coincide with clusters)?Which are the exceptions (‘misplaced nodes’)?
Reading principle:
Indentify what is in the center- of the graph- of each clusterIdentify 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
Central nodes and clusters
Bridging nodes and clusters
Technical step:The ranking palette
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 positionWhich 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 connectivityWhich nodes are the authorities (receive most connections)?Which nodes are the hub (originate most connections)?
C. Color (color by partition)
4. DistributionIs typology coherent with topology (partitions coincide with clusters)?Which are the exceptions (‘misplaced nodes’)?
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
Authorities
Hubs
Technical step:Data laboratory window Gephi.org
Technical step:the partition palette
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 positionWhich 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 connectivityWhich nodes are the authorities (receive most connections)?Which nodes are the hub (originate most connections)?
C. Color (color by partition)
4. DistributionIs typology coherent with topology (partitions coincide with clusters)?Which are the exceptions (‘misplaced nodes’)?
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
Typology and topology
Exceptions
Polarization
Polarization
Visual network analysis
Visual network analysis
Venturini, T., Jacomy, M, De Carvalho Pereira, D.
Visual Network Analysis: The example of the rio+20
online debate (working paper)
Part VI The médialab
toolbox
The médialabtoolkit http://tools.medialab.sciences
-po.fr
The médialab toolkit
The médialab toolkit
Sciencescape http://tools.medialab.sciences-po.fr/sciencescape/
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
Sciencescape Journal over time(ANT from Scopus)
Sciencescape Keyword over time(ANT from Scopus)
Sciencescape Authors-Keywords-Journals sankey
(ANT from Scopus)
Sciencescape Keywords’ network(ANT from Scopus)
Sciencescape Future developments
Table2Net http://tools.medialab.sciences-po.fr/table2net/
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
Table2Net http://tools.medialab.sciences-po.fr/table2net/
Normal
Bipartite
Hyphe http://hyphe.medialab.sciences-po.fr/demo/
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
Hyphe Flexible definition of ‘web-entities’
Hyphe Flexible definition of ‘web-entities’
HypheSemi-automatic semi-manual crawling
HypheSemi-automatic semi-manual crawling
Hyphe The future interface(under construction)
ANTAactor-networktext analyzer http://jiminy.medialab.sciences-po.fr
/anta_dev/
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
ANTAactor-networktext analyzer http://jiminy.medialab.sciences-po.fr
/anta_dev/
ANTA http://jiminy.medialab.sciences-po.fr/anta_dev/
ANTAactor-networktext analyzer http://jiminy.medialab.sciences-po.fr
/anta_dev/
ANTA http://jiminy.medialab.sciences-po.fr/anta_dev/
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/
The médialab toolkit
Gephihttps://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
Heatgraph From networksto heatmaps
RÉFÉRENCE
Turing AM, 1952, Phil.
Trans. of the Royal Society
of Bio. Sciences
INSTITUTIONspecialisée
Blackett Lab. ImperialCollege
MOT CLE
Magnetic properties
INSTITUTIONnon-
specialisée
Ecole Polytechnique
de Zurich
Heatgraph Ego-centered heatgraphs
Heatgraph http://tools.medialab.sciences-po.fr/heatgraph/
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
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