Teaching excellence for over a hundred years

The structure of social network sites:a comparison between Facebook and LinkedIn

– preliminary results –

Ivana Pais – University of Brescia (pais@jus.unibs.it)

Riccardo De Vita – University of Greenwich

Roberto Marmo – University of Pavia

Workshop on Advanced Research MethodsSeptember 30, 2010

Teaching excellence for over a hundred years

Agenda

Social network sites: does one structure fit all?

Empirical setting: the case of Milan In

Methodology: exploring the data

Preliminary findings: different levels of analysis

Discussion and conclusion: the next steps

Teaching excellence for over a hundred years

Theoretical background

‘Appropriable social organisations’ vs intentional organizations (Coleman, 1990)

Technological development online social capital

Social networks are embedded in the variety ofcommunication artifacts available in the currenttechnoscape (Licoppe, Smoreda 2005) different formsof Social Network Sites : registration-based vs. connection based, social vs. professional, online vs. offline…(O’Murchu, Breslin, Decker, 2004; boyd, Ellison 2007)

Teaching excellence for over a hundred years

Research question

The majority of the studies focused on the understanding of the effects of social networks; while the factors that generate, sustain and reproduce them partly remain to be explored (Smith-Doerr & Powell, 2005).

Little is known about the specific structure of online social network services (Lewis et al. 2008)

RQ: Are different social network sites associated withdifferent network structures?

Teaching excellence for over a hundred years

Milan In

A non-profit association set up in 2005 to allow members of LinkedIn living in Milan to physically meet up with each other.

Comparative study: o same organization & same actorso Linkedin Group Vs Facebook Group

4311 1357505

Teaching excellence for over a hundred years

Method

Structural Variables: connection on Facebook and Linkedin groups symmetric networks

Composition variable: gender, education, job title, number of connections,...

Exploratory analysis of several network properties at the global and local level

Software: UCINET 6 (Borgatti, Everett and Freeman, 2002) and helper applications

Linkedin Group – Members since 2005

…adding the members since 2006

…adding the members since 2007

…adding the members since 2008

…adding the members since 2009

Linkedin Group – Today

Linkedin vs Facebook GroupMan; Woman

N Components Isolates Density Centralization Avg Degree505 6 5 0.027** 83.4 % 13.5

505 35 33(+1 dyad)

0.019** 79.0 % 9.43

Multiplexity

% of ties in theLinkedin Group

% of ties in theFacebook Group

2.00% 2.86%

Clique size

Overall Clustering CoefficientFacebook Group 0.584Linkedin Group 0.494

Identifying relevant actors

Degree CentralityTop 5 actors in Linkedin Group

ID Facebook Linkedin

344 0 432347 7 260276 2 101

1031 5 9116 25 90

5 Key PlayersKPP 2 – Using nodes

Facebook Group Linkedin Group

714 34419 16

394 321482 1101

1236 530

Degree CentralityTop 5 actors in Facebook Group

ID Facebook Linkedin

19 406 3886 139 7620 87 3696 75 2497 74 40

Teaching excellence for over a hundred years

Discussion

Different network structures are associated with online groups built for the same purpose but on different platforms

o Specialization and different behaviour

Implications for academic debate and for organization management

Need to take a process perspective in analyzing network evolution

Teaching excellence for over a hundred years

The next steps…

Data:o Relations recommendations, physical interactiono Attribute data questionnaire and deeper analysiso Homophily

Methodological approach:o Longitudinal analysis

Theoretical perspective:o Network structure and organizational development

Empirical setting:o Comparison with other online social networks