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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
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