When a Movement Becomes a Party: Computational Assessment of New Forms of Political Organization in...

When a Movement Becomes a PartyComputational Assessment of New Forms of

Political Organization in Social Media

Pablo Aragón*†, Yana Volkovich†, David Laniado†, Andreas Kaltenbrunner†

* Universitat Pompeu Fabra † Eurecat

The 10th International AAAI Conference on Web and Social Media (ICWSM-16)

Cologne, Germany, May 17-20

Motivation: Movement organizations

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Networked social movement: Networks in multiple forms (multimodal, on/offline, across platforms) without a central node, and with a decentralized structure.

(Castells, 2013)

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

(Bennett et al, 2013)

Motivation: Movement networks

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“Decentralized structure, based on coalitions of smaller organizations”.

(González-Bailón et al, 2011)

“Decentralized organization without stable leaders or representatives”.

(Aragón et al, 2013)

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

Motivation: Party organizations

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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

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The Twitter party networks in the 2011 Spanish election presented:

Isolated clusters for each party.

Every party cluster was strongly centralized around candidate and/or party accounts.

(Aragón et al, 2013)

RT network of political parties in the 2011 Spanish election (Aragón et al, 2013)

Motivation: The 2015 Spanish local elections

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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

Research Question

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Assuming that:

Barcelona en Comú emerged from the 15M movement

the 15M movement followed a decentralized structure

Has Barcelona en Comú…

preserved a decentralized structure?

adopted a conventional centralized organization?



Dataset

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“Political parties share some interesting patterns of behavior, but also exhibit some 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

Computational assessment

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

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

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

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Every cluster contains some media accounts

Each execution produces different results:Some media do not always belong to the same cluster

Community detection

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Community detection

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Community detection

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Community detection

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We want the real intra-network structure of parties

Community detection

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We want the real intra-network structure of parties

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 times (1-ε=0.95)

Community detection

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Community detection

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Result with the Louvain Method with Confidence Interval

Constant presence of eight major clusters (seven parties) along the 100 executions

Community detection

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Most media accounts are now excluded from major clusters

Community detection

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Two clusters for Barcelona en Comú …

Community detection

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Two clusters for Barcelona en Comú …

Movement-party structure

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Party

Movement



Visualization of the two clusters of Barcelona en Comú

Cluster characterization

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

Hierarchical structure

In-degree centralization Gini coefficient of the in-degree distribution

Small world phenomenon (f.k.a. information efficiency)

Avg. path length + Clustering coefficient

Coreness (f.k.a. social resilience)

Maximal k-core Distribution of k-indices

Cluster characterization: Hierachical structure

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Gini coefficient of the in-degree distribution

Cluster characterization: Small world

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Cluster characterization: Coreness

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Conclusions

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For Barcelona en Comú, two paradigms co-exist:

A centralized and low resilient party cluster

A decentralized and resilient movement cluster

Polarized scenario likes 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

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How did the dual network structure Barcelona en Comú was built over time?

As the result of 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 Comú? … Syriza?

Is this dual paradigm observable in other OSNs?

Facebook? Youtube? Instagram?

References

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

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.

González-Bailón, S., Borge-Holthoefer, J., Rivero, A., and Moreno, Y. (2011). The dynamics of protest recruitment through an online network. Scientific reports, 1.

Aragón P., Congosto M., & Laniado D. (2013). 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 spanishnational election. Policy & Internet, 5(2):183–206.

References

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Michels, R. (1915). Political parties: A sociological study of the oligarchical tendencies of modern democracy. Hearst’s International Library Company.

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

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

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

Lietz, H., Wagner, C., Bleier, A., & Strohmaier, M. (2014). When Politicians Talk: Assessing Online Conversational Practices of Political Parties on Twitter.

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.

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.

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