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Online Social Networks

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  • 1. The Analysis of Online Social Networks Sandra Gonzalez‐Bailon sandra.gonzalezbailon@oii.ox.ac.uk d l b il ii k
  • 2. The Questions We Will Consider Today: The Questions We Will Consider Today: • How can we represent online interactions How can we represent online interactions  as networks? • Wh Why would we want to do that? ld d h ? • What are the features we should look for? What are the features we should look for?
  • 3. The Questions We Will Consider Today: The Questions We Will Consider Today: • How can we represent online interactions How can we represent online interactions  as networks? • Wh Why would we want to do that? ld d h ? • What are the features we should look for? What are the features we should look for? What we will  not consider: What we will *not* consider:  how to get the data (crawling,  pp g q y g screen scrapping, querying SQL  databases...)
  • 4. The Elements of a Network: Nodes and Edges The Elements of a Network: Nodes and Edges
  • 5. The Elements of a Network: Nodes and Edges The Elements of a Network: Nodes and Edges Who are the nodes and what are the edges?
  • 6. Same Nodes, Different Relationships Same Nodes, Different Relationships S S S D L D L D L Q J G Q J G Q J G P L P L P L J J J SDP2010 network Friendship network College network
  • 7. What Hides behind a Network... What Hides behind a Network... friendship network 9 2 2 college network 3 9 3 8 1 4 8 1 4 7 5 7 5 6 6 1 2 3 4 5 6 7 8 9 1 2 3 4 5 6 7 8 9 1 0 0 0 0 1 1 1 1 1 0 0 0 1 1 1 1 1 2 0 1 0 0 0 0 0 0 2 0 1 0 0 0 0 0 0 3 0 1 0 0 0 0 0 0 3 0 1 0 0 0 0 0 0 4 0 0 0 0 0 0 0 1 4 0 0 0 0 0 0 0 0 5 0 0 0 0 1 0 0 0 5 1 0 0 0 1 1 1 1 6 1 0 0 0 1 0 0 0 6 1 0 0 0 1 1 1 1 7 1 0 0 0 0 0 0 0 7 1 0 0 0 1 1 1 1 8 1 0 0 0 0 0 0 0 8 1 0 0 0 1 1 1 1 9 1 0 0 1 0 0 0 0 9 1 0 0 0 1 1 1 1
  • 8. Different Networks with the Same Type of Nodes Different Networks with the Same Type of Nodes Adamic and Glance (2005) Hargittai et al. (2008) Butts and Cross (2009)
  • 9. First Lesson First Lesson Define very carefully Define very carefully • who are the nodes? sampling issues who are the nodes? – sampling issues • what are the edges? – measurement issues
  • 10. Selecting your population  Selecting your population – snowballing S Q G J
  • 11. Selecting your population  Selecting your population – snowballing S L S Q G Q G P L J P J
  • 12. Selecting your population  Selecting your population – snowballing S S S L S Q G Q G P L J P J
  • 13. Selecting your population  Selecting your population – snowballing
  • 14. Selecting your population  Selecting your population – snowballing
  • 15. Selecting your population  Selecting your population – snowballing 3 2 1 4
  • 16. Remember! Different Sampling Methods,  Different Networks! Butts and Cross (2009) “Change and External Events in Computer-Mediated Citation Networks: English Language Weblogs and the 2004 U.S. Electoral Cycle”, Journal of Social Structure, 10(3)
  • 17. What are the edges? Defining the relationships What are the edges? Defining the relationships Research Institutes Health Charities NGOs Media Religious Organisations Environment Education Security Activism Intergovernmental UN Agencies Sports Associations S t A i ti Professional Associations Adamic and Adar (2003) Friends and ( ) Gonzalez-Bailon (2009) Social Factors Networks on the Web, Social Networks, Underlying the Structure of the Web, Social 25(3): 211-230 Networks, 31(4): 271-280
  • 18. What are the edges? Defining the relationships What are the edges? Defining the relationships Three Ways of Measuring Friendship (three types of links) y g p( yp ) Lewis et al. (2008) ʺTastes, ties, and time: A new social network dataset using  Facebook.com.ʺ Social Networks 30:330‐42
  • 19. What are the edges? Defining the relationships What are the edges? Defining the relationships the Difference between Weak and Strong Ties Choudhury, M.D., W.A. Mason, J.M. Hofman, Choudhury M D W A Mason J M Hofman and D J Watts (2010). "Inferring Relevant Social D.J. Watts. (2010) Inferring Networks from Interpersonal Communication" in Proceedings of the 19th international conference on World Wide Web.
  • 20. Common Network Statistics Common Network Statistics • Mean degree • F ti Fraction of nodes in largest component f d i l t t • Geodesic distance • Cl t i Clustering coefficient (transitivity) ffi i t (t iti it ) • Degree correlation coefficient 
  • 21. Social vs Technological Networks g Newman (2003) “The Structure and Function of Complex Networks”, arXiv:cond-mat/0303516 v1
  • 22. Why Should we Care about these Network Stats? Why Should we Care about these Network Stats?
  • 23. Why Should we Care about these Network Stats? Why Should we Care about these Network Stats? • Networks shape the flow of information Networks shape the flow of information • the most central websites and blogs are the most visible • the eco chamber effect the eco‐chamber effect  • Networks channel social influence and contagion • viral marketing viral marketing • collective action and mobilisations • Networks allow us to understand social Networks allow us to understand social  interactions better
  • 24. Why Should we Care about these Network Stats? Why Should we Care about these Network Stats? • Networks shape the flow of information Networks shape the flow of information • the most central websites and blogs are the most visible • the eco chamber effect the eco‐chamber effect  • Networks channel social influence and contagion • viral marketing viral marketing • collective action and mobilisations • Networks allow us to understand social Networks allow us to understand social  interactions better • Are citizens becoming increasingly isolated? Are citizens becoming increasingly isolated?
  • 25. What Surveys Tell us about Personal Networks What Surveys Tell us about Personal Networks Putnam (1995) "B li Al P t "Bowling Alone: A America's D li i S i l C it l " J i ' Declining Social Capital." Journal of l f Democracy 6:65-78.
  • 26. What Surveys Tell us about Personal Networks What Surveys Tell us about Personal Networks Paxton (1999) "Is Social Capital Declining in the United States? A Multiple Indicator Assessment." American Journal of Sociology 105:88-127.
  • 27. What Surveys Tell us about Personal Networks What Surveys Tell us about Personal Networks McPherson et al (2006) "Social Isolation in America: Changes in Core Discussion al. Social Networks over Two Decades." American Sociological Review 71:353-375.
  • 28. Research Questions Research Questions • Are discussion networks really shrinking?  Measurement artefact? Measurement artefact? • How stable is participation in discussion How stable is participation in discussion  networks over time?   Gonzalez‐Bailon (2010) “The Online Response to Offline Disengagement. The  Growth of Internet‐Enabled Political Discussion Networks (1999‐2005)”,  under review
  • 29. The Data: Usenet Discussions comp.* p misc.* news.* rec.* sci.* soc.* talk.* comp.software rec.music talk.religion comp.sys.mac rec.art.movies i lk li i talk.politics … … … Smith, Marc. 1999. ʺInvisible Crowds in Cyberspace: Mapping the Social  Structure of the Usenet.ʺ in Communities in Cyberspace, edited by M. Smith  and P. Kollock. London: Routledge.
  • 30. The Data: Usenet Discussions comp.* p misc.* news.* rec.* sci.* soc.* talk.* comp.software rec.music talk.religion comp.sys.mac rec.art.movies i lk li i talk.politics … … … usa news uk ‘politics’ us soc org free regionalism ‘politica’ es charla local hipcrime forums groups democrats homosexuality immigration discussioni 935 groups crimehip 935 groups marxism world gmane arms-d nationalism 97 groups 97 groups italia clari agora internet taxation peace anti fascism anti-fascism natl-socialism gov parties assassination guns alt news philosophy hil h 670,000 users extremism emircpih socialism philosophy rent-control lang fido libertarian circumcision england tax-ev asion 89,000 users internazionale polo lega-nord destra fidonet forums clinton party -of -the-unacanceller tw can hatemongers sc culture referendum crime soc web liberal co national-socialist grinch animals europe suck bleed alleanza-nazionale politically editorial web reti msn bologna republican soviet bush general drugs votelink constitution chinese personalities murders commentary misc republicans white president crypto newsguy d scu bc fan european-union activism pa military efnet mideast chess infotimes cattolici fido pt v erdi politicas kharkov cna liberalism newt freenet conservative fsu politicscn irc games progressive class media religion black git discuss pnet pubforum discussion nuov ipartiti sesso esp correct wankers congress tibet sci texas ab announce green wales elections medicine arabic theory hk ie ilt christian-democrat pakistan international corruption candidate amend2 sk yps people india environment asu za
  • 31. Thousands x 100 000 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 19990901 1 19990901 19991201 1 19991201 20000301 1 200 000301 20000601 1 200 000601 Bush vs Gore 20000901 1 200 000901 20001201 1 200 001201 Berlusconi vs Rutelli 20010301 1 200 010301 20010601 1 200 010601 20010901 1 200 010901 20011201 1 200 011201 20020301 1 200 020301 (a) politics (b) politica 20020601 1 200 020601 20020901 1 200 020901 20021201 1 200 021201 invasion Iraq 20030301 1 200 030301 Number of discussions started Number of discussions started 20030601 1 200 030601 20030901 1 200 030901 Madrid bombs 20031201 1 200 031201 Zapatero vs Rajoy 20040301 1 200 040301 20040601 1 200 040601 Bush vs Kerry 20040901 1 200 040901 20041201 1 200 041201
  • 32. How are Networks Reconstructed? How are Networks Reconstructed?
  • 33. Distribution of Ego Networks  Distribution of Ego‐Networks Network Size ‘politics’ 10 8 6 4 2000 2001 2002 2003 2004 2005 time 10 ‘politica’ 8 6 4 2000 2001 2002 2003 2004 2005 time
  • 34. Transitivity of Ego Networks (Clustering)  Transitivity of Ego‐Networks (Clustering) Transitivity ‘politics’ politics 0.30 0.24 0.18 2000 2001 2002 2003 2004 2005 time ‘politica’ 0.30 0.15 0 2000 2001 2002 2003 2004 2005 time
  • 35. (a) politics (N~280,000) The Effects of  The Effects of Responses received Online  Discussions started Transitivity Networks  Networks Size personal network Messages sent on Length of  Commitment C it t -0.6 06 R^2 = 0.114 -0.4 04 -0.2 02 0.0 00 0.2 02 (b) politica (N~20,000) Responses received Transitivity Size personal network Discussions started Messages sent -0.6 -0.4 -0.2 0.0 0.2 R^2 = 0.149
  • 36. The Effects of Online Networks on Intensity of  The Effects of Online Networks on Intensity of Commitment
  • 37. Users Don t Stay for Long, however... Users Don’t Stay for Long, however... ‘politics’ (N~336,000) p ( , ) ‘politica’ (N~23,000) p ( ) Survival Probability Survival Probability 0.8 8 0.8 8 One One More than one More than one 0.4 0.4 0.0 0.0 0 10 20 30 40 50 60 0 10 20 30 40 50 60 Months Months
  • 38. Research Questions Research Questions • Are discussion networks really shrinking?  No – d li N and online networks also have a positive  t k l h iti impact on engagement • How stable is participation in discussion How stable is participation in discussion  networks over time?   Not much – difficult to tell if more or less than  Not much difficult to tell if more or less than offline (we don’t have offline data) 
  • 39. Let’s assess the example: • Who are the nodes? • What is the meaning of links? What is the meaning of links? • What is the sampling strategy?  •HHow is the time dimension dealt with?  i h i di i d l i h? • What are the network features analysed?
  • 40. If you need more info... If you need more info Hansen, D., B. Shneiderman, and M. Smith (2010).   Analyzing Social Media Networks with NodeXL.  y g Morgan Kaufmann Newman, M.E.J. (2010). Networks: An Introduction,  y , , Oxford University Press, Oxford, UK