People, Places, and Personal Networks: a Reconceptualization of

some Social Phenomena

José Luis Molina (UAB), Miranda J. Lubbers (UAB), and Raffele Vacca (University of Florida)

MoReMi Seminars - Toulouse – April 19, 2013

MICINN - CSO2012-32635 Emprendimiento Social: Embeddedness

Local, Social Networking Sites y desarrollo teórico (ENCLAVE).

Key ideas

• Big data for social sciences provides tipically information about people linked to people (social networks), and places (geo-localization).

• Big data has been used so far for testing extant social concepts or for applied – commercial purposes.

Key ideas (2)

• Social Theory should be enhanced with the new empiric data available, specially in the realm of collective behavior, crowds or networks of heterogeneous people, and places.

• As far as personal network data is now “native” digital data, we will suggest ways to have into account both diversity in people, and places.

Fields of application

• Collective behavior in case of disasters (Lu et al. 2011, Bagrow et al. 2011, Candia et al. 2008).

• Prediction of social ties from a panel of mobile users (Onnela et al. 2007, Crandall et al. 2010)

• Testing the Dunbar number for electronic communications (Gonçalves et al. 2011)

• Language use in whole countries …

Belgium, 2,5 M Orange users, 6 Months

Lambiotte, Renaud, Vincent D. Blondel, Cristobald de Kerchove, Etienne Huens, Christophe Prieur, Zbigniew Smoreda, Paul Van Dooren (2008). "Geographical dispersal of mobile communication network", arXiv:0802.2178v2 [physics.soc-ph] .

Mocanu, Delia et al. (2012). The Twitter of Babel: Mapping World Languages through Microblogging Platforms, arXiv:1212.5238v1 [physics.soc-ph] 20 Dec 2012

Balance

• New and valuable insights.

• Testing extant social theories (Homophily, Small World, Strong/Weak ties, Core-periphery structures in personal networks, difussion …).

• Bivariate comparisons (i.e. GNP-twitter users), predictions of tie, or tie persistence.

• Applied research (emergencies, crisis, disasters).

• Recommender systems.

What about new Social Theory?

• We have now big data that links systematically people to people, and people to places, right?

People, places, and networks

Single place

People Homogeneous

Community Gemeinschaft Mechanic solidarity

People Diverse

Gesellschaft Organic solidarity Organizations Public sites Crowds

People, places, and networks

Single place Different Places

People Homogeneous

Community Gemeinschaft Mechanic solidarity

Migration mobility

People Diverse

Gesellschaft Organic solidarity Organizations Public sites Crowds

Super-diversity

People, places, and networks

Single place Places Origin Destination Origin and

Destination

People Homogeneous

Community Gemeinschaft Mechanic solidarity

Migration mobility

Diaspora Enclave Transnational field /space

Circular migration …

People Diverse

Gesellschaft Organic solidarity Organizations Public sites Crowds

Super-diversity

- - -

Sikhs in Barcelona

Indians shops in Lloret de Mar

Indians in Lloret de Mar (2)

Places

Spatial segregation high Spatial segregation low

People Homogeneous

Neighborhoods Enclaves Ghettoes

Condominiums Colonies

-

People Diverse

Cluster

Cosmopolitanism

Cluster textil - Uriangato-Moroleón (México)

Alejandro García-Macías

So far …

• We have a rich set of social concepts for describing social phenomena relating homogenous people in a single or different places, but

• we have a few wide concepts for describing social phenomena relating heterogeneous people in a single or different places (“cosmopolitanism”, “crowds”, “collective behavior”, “super-diversity”...).

• Big data analyses has focused so far in existing social concepts or in a commercial/applied goals.

A personal network approach

1. Personal network data has typically ego attributes, alter attributes, and alter-alter ties.

2. Diversity can be measured at the network level (i.e. place of residence) or

3. at the subgroup level (i.e. those related live in different countries).

Relating diversity, and structure

Vacca, Raffaele (2013). Bridging Across Nations. The social capital of diversity, brokerage and closure in transnational migrant networks: a study on assimilation patterns in Milan and Barcelona. Dissertation defended at the Università degli Studi di Milano-Bicocca , 12-03-2013.

Segregation index (Vacca 2013)

• H = Ḣ−Ĥ

Ḣ

Ḣ: diversity at network level, Ĥ: diversity at subgroup level.

• Network A= close to maximum (group/structural) segregation (enthropy), close to 0.

• Network D = close to minimum segregation (enthropy), close to 1.

Pooling all together

• Combining and analyzing personal network data gathered from app and other sources we can contribute to the development of new Social Theory…

• If we have the appropriate research questions!

Merci!