Computational Framework for the Assessment of New Forms of

Political Organization in Social Media

Pablo Aragón Asenjo

Máster en I.A. Avanzada

Universidad Nacional de Educación a Distancia

Supervisors

Andreas Kaltenbrunner

Luis Manuel Sarro Baro

Index

2Pablo Aragón Asenjo – Computational Framework for the Assessment of New Forms of Political Organization in Social Media

Motivation

Computational Framework

Dataset

Community detection

Cluster characterization

Conclusions and Future Work

Dissemination

Motivation: Movement organizations

3Pablo Aragón Asenjo – Computational Framework for the Assessment of New Forms of Political Organization in Social Media

Networked social movement: Networked in multiple forms (on/offline,

across platforms) without a central node, and with a decentered structure.

(Castells, 2013)

Change from logic of collective action to a logic of connective action.

(Bennett & Segerberg, 2013)

Motivation: Movement networks

4Pablo Aragón Asenjo – Computational Framework for the Assessment of New Forms of Political Organization in Social Media

“Decentralized structure,

based on coalitions of smaller

organizations”.

(González-Bailón et al, 2011)

“Decentralized organization,

without leaders or stable

representatives”.

(Aragón et al, 2015)

RT network of the 15M movementMay 15-22, 2011 (Aragón et al, 2015)

Motivation: Party organizations

5Pablo Aragón Asenjo – Computational Framework for the Assessment of New Forms of Political Organization in Social Media

Iron Law of Oligarchy: Political parties, like any complex organization, self-

generate an elite (“Who says organization, says oligarchy”).

(Michels, 1915)

Elite theory: Small minorities (elites) hold the most power in political processes.

(Pareto et al, 1935; Mosca, 1939; Mills 1999)

Motivation: Party networks

6Pablo Aragón Asenjo – Computational Framework for the Assessment of New Forms of Political Organization in Social Media

In the Twitter party networks for

the 2011 Spanish election:

Isolated clusters for each party.

Minor/new parties were more

clustered and better connected.

Every party cluster was strongly

centralized around candidate

and/or party profiles.

(Aragón et al, 2013)RT network of political parties in

the 2011 Spanish election (Aragón et al, 2013)

Motivation: The 2015 Spanish local elections

7Pablo Aragón Asenjo – Computational Framework for the Assessment of New Forms of Political Organization in Social Media

Grassroots parties emerged from the 15M movement:

Barcelona en Comú

Ahora Madrid

Zaragoza en Común

Marea Atlántica

Compostela Aberta

Por Cádiz Sí se puede

Guanyem Badalona en Comú

Motivation: Research Question

8Pablo Aragón Asenjo – Computational Framework for the Assessment of New Forms of Political Organization in Social Media

Assuming that:

Barcelona en Comú emerged from the 15M

the 15M movement followed a decentralized structure

Has Barcelona en Comú…

preserved a decentralized structure?

adopted a conventional centralized organization?

Computational Framework

9Pablo Aragón Asenjo – Computational Framework for the Assessment of New Forms of Political Organization in Social Media

Community detection Identify the organization of nodes in clusters: political party networks.

Cluster characterizationCharacterize the topology of the intra-network of each cluster.

“Political parties share some interesting patterns of behavior, but also exhibitsome unique and interesting idiosyncrasies” (e.g. tagging practice of politicians)

(Lietz et al, 2015)

Sampling criteria based on candidate and party accounts:

373 818 RTs

RT network

6 492 nodes

16 775 edges

Dataset

10Pablo Aragón Asenjo – Computational Framework for the Assessment of New Forms of Political Organization in Social Media

Community detection (I)

11Pablo Aragón Asenjo – Computational Framework for the Assessment of New Forms of Political Organization in Social Media

First result with the Louvain Method (Blondel et al, 2008):

Eight major clusters (seven parties)

Every cluster contains some media accounts: media build weak ties

Analysis of the ego-network of relevant media accounts:

Public TV account retweeted by users from every cluster

Private media mostly retweeted by users from like-minded parties

Community detection (II)

12Pablo Aragón Asenjo – Computational Framework for the Assessment of New Forms of Political Organization in Social Media

Every cluster contains some media accounts

Each execution produces different results:

Some media do not always belong to the same cluster

The real party intra-network structure is needed

Community detection (II)

13Pablo Aragón Asenjo – Computational Framework for the Assessment of New Forms of Political Organization in Social Media

Every cluster contains some media accounts

Each execution produces different results:

Some media do not always belong to the same cluster

The real party intra-network structure is needed

Community detection (II)

14Pablo Aragón Asenjo – Computational Framework for the Assessment of New Forms of Political Organization in Social Media

Every cluster contains some media accounts

Each execution produces different results:

Some media do not always belong to the same cluster

The real party intra-network structure is needed

Community detection (II)

15Pablo Aragón Asenjo – Computational Framework for the Assessment of New Forms of Political Organization in Social Media

Every cluster contains some media accounts

Each execution produces different results:

Some media do not always belong to the same cluster

The real party intra-network structure is needed

Community detection (II)

16Pablo Aragón Asenjo – Computational Framework for the Assessment of New Forms of Political Organization in Social Media

Every cluster contains some media accounts

Each execution produces different results:

Some media do not always belong to the same cluster

The real party intra-network structure is needed

Louvain Method with Confidence Interval

Run multiple executions (N=100)

Validate the stability of major clusters

Just consider nodes that appear in the same cluster more than 1-ε (ε=0.05)

Community detection (III)

17Pablo Aragón Asenjo – Computational Framework for the Assessment of New Forms of Political Organization in Social Media

Results with the Louvain Method

with Confidence Interval:

Constant presence of eight major

clusters (seven parties) along the

100 executions.

Community detection (III)

18Pablo Aragón Asenjo – Computational Framework for the Assessment of New Forms of Political Organization in Social Media

Results with the Louvain Method

with Confidence Interval:

Constant presence of eight major

clusters (seven parties) along the

100 executions.

Most media accounts do not

appear in major clusters

Community detection (III)

19Pablo Aragón Asenjo – Computational Framework for the Assessment of New Forms of Political Organization in Social Media

Results with the Louvain Method

with Confidence Interval:

Constant presence of eight major

clusters (seven parties) along the

100 executions.

Most media accounts do not

appear in major clusters

Two clusters for Barcelona

en Comú

Community detection (IV)

20Pablo Aragón Asenjo – Computational Framework for the Assessment of New Forms of Political Organization in Social Media

Party

Movement

Community detection (V)

21Pablo Aragón Asenjo – Computational Framework for the Assessment of New Forms of Political Organization in Social Media

Comparison to the

Clique Percolation Method

(Palla et al, 2005)

Similar results but…

CPM is O(exp(n))

(NP-complete problem)

CPM is not sensitive to different

sizes and structures of parties

K-cliques are only the core of

the structure of party networks

(the periphery is relevant)

Cluster characterization

22Pablo Aragón Asenjo – Computational Framework for the Assessment of New Forms of Political Organization in Social Media

Inspired by the social dimensions of García et al (2015):

Hierarchical structure

Small world

(Information efficiency)

Coreness

(Social Resilience)

Cluster characterization: Hierarchical structure (I)

23Pablo Aragón Asenjo – Computational Framework for the Assessment of New Forms of Political Organization in Social Media

Original In-degree centralization (Freeman, 1979)

Proposed Gini coefficient of the in-degree distribution

Cluster characterization: Hierarchical structure (II)

24Pablo Aragón Asenjo – Computational Framework for the Assessment of New Forms of Political Organization in Social Media

Original In-degree centralization (Freeman, 1979)

Proposed Gini coefficient of the in-degree distribution

Cluster characterization: Small world (I)

25Pablo Aragón Asenjo – Computational Framework for the Assessment of New Forms of Political Organization in Social Media

Original Average path length

Proposed Average path length + Clustering coefficient

Cluster characterization: Small world (II)

26Pablo Aragón Asenjo – Computational Framework for the Assessment of New Forms of Political Organization in Social Media

Original Average path length

Proposed Average path length + Clustering coefficient

Cluster characterization: Coreness (I)

27Pablo Aragón Asenjo – Computational Framework for the Assessment of New Forms of Political Organization in Social Media

Original Maximal k-core

Proposed Distribution of k-indices

Cluster characterization: Coreness (II)

28Pablo Aragón Asenjo – Computational Framework for the Assessment of New Forms of Political Organization in Social Media

Original Maximal k-core

Proposed Distribution of k-indices

Conclusions (I)

29Pablo Aragón Asenjo – Computational Framework for the Assessment of New Forms of Political Organization in Social Media

Contribution of the computational framework to the state-of-the-art:

Community detectionThe confidence interval identifies clusters in a more stable way

Cluster characterizationNew metrics better capture the social dimensions of clusters

Conclusions (II)

30Pablo Aragón Asenjo – Computational Framework for the Assessment of New Forms of Political Organization in Social Media

For Barcelona en Comú, two paradigms co-exist:

A centralized and low resilient party cluster

A decentralized and resilient movement cluster

Polarization like previous studies of election campaigns on Twitter

Data preparation process accentuated the polarization effect

Media accounts build weak ties between clusters

Public media became more plural than private media

Open questions and future work

31Pablo Aragón Asenjo – Computational Framework for the Assessment of New Forms of Political Organization in Social Media

How was the dual network structure of Barcelona en Comú built?

As the confluence of minor parties and the 15M activists?

As a party interface over a citizen decentralized system?

Is this dual paradigm observable in other grassroots parties?

Ahora Madrid? Zaragoza en Común? … Syriza?

Is this dual paradigm observable in other OSNs?

Facebook? Youtube? Instagram?

Dissemination: D-CENT project

32Pablo Aragón Asenjo – Computational Framework for the Assessment of New Forms of Political Organization in Social Media

This work has been funded by the

EU research project D-CENT

(FP7 CAPS 610349)

The results are contained in the

project deliverable “D2.3 - When a

movement becomes a party”

Dissemination: ICWSM-16

33Pablo Aragón Asenjo – Computational Framework for the Assessment of New Forms of Political Organization in Social Media

This research was accepted as a full

paper at the International Association

for the Advancement of Artificial

Intelligence Conference on Web and

Social Media (ICWSM-2016)

Double-blind peer review process

17% full paper acceptance rate

References (I)

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Aragón P., Congosto M., & Laniado D. (2014). Evolución del sistema- red 15m a través de

topología de redes. In Toret, J., Calleja, A., Marín, O., Aragón, P., Aguilera, M.,

Barandarian, X., Lumbreras, A. & Monterde, A. (2015). Tecnopolítica y 15M. La

potencia de las multitudes conectadas, Barcelona: Editorial UOC. ISBN: 978-84-9064-

458-4.

Aragón, P., Kappler, K. E., Kaltenbrunner, A., Laniado, D., and Volkovich, Y. (2013).

Communication dynamics in twitter during political campaigns: The case of the 2011

spanish national election. Policy & Internet, 5(2):183–206.

Bennett, W. L. and Segerberg, A. (2012). The logic of connective action: Digital media and

the personalization of contentious politics. Information, Communication & Society,

15(5):739–768.

References (II)

35Pablo Aragón Asenjo – Computational Framework for the Assessment of New Forms of Political Organization in Social Media

Blondel, V. D., Guillaume, J.-L., Lambiotte, R., and Lefebvre, E. (2008). Fast unfolding of

communities in large networks. Journal of Statistical Mechanics: Theory and

Experiment, 2008(10):P10008.

Castells, M. (2013). Networks of outrage and hope: Social movements in the Internet age. J.

Wiley & Son

Freeman, L. C. (1979). Centrality in social networks conceptual clarification. Social

networks, 1(3), 215-239.

Garcia, D., Abisheva, A., Schweighofer, S., Serdult, U., and Schweitzer, F. (2015). Ideological

and temporal components of network polarization in online political participatory

media. Policy & Internet, 7(1):46–79.

González-Bailón, S., Borge-Holthoefer, J., Rivero, A., and Moreno, Y. (2011). The dynamics of

protest recruitment through an online network. Scientific reports.

References (III)

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Lietz, H., Wagner, C., Bleier, A., & Strohmaier, M. (2014). When Politicians Talk: Assessing

Online Conversational Practices of Political Parties on Twitter.

Michels, R. (1915). Political parties: A sociological study of the oligarchical tendencies of

modern democracy. Hearst’s International Library Company.

Mills, C. W. (1999). The power elite. Oxford University Press.

Mosca, G. (1939). The ruling class: elementi di scienza politica.

Palla, G., Derényi, I., Farkas, I., and Vicsek, T. (2005). Uncovering the overlapping community

structure of complex networks in nature and society. Nature, 435(7043):814– 818.

Pareto, V., Livingston, A., Bongiorno, A., Rogers, J. H., et al. (1935). Mind and society

Q & A

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