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SIGSNA: Special Interest Group on Social Network Analysis Luca Rossi - Fabio Giglietto University of Urbino “Carlo BO”

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Presentation on theme: "SIGSNA: Special Interest Group on Social Network Analysis Luca Rossi - Fabio Giglietto University of Urbino “Carlo BO”"— Presentation transcript:

1 SIGSNA: Special Interest Group on Social Network Analysis Luca Rossi - Fabio Giglietto University of Urbino “Carlo BO”

2 persistence/ easy to search/ addressed to an unknowable audience/ easy to replicate/ (boyd 2007) Background: Growing availability of User Generated Content High research value of spontaneously produced contents.

3 persistenceinvisible audiencesscalabilitysearchability fewWritingsPrinting press, newspapers Digital media (pc, video- cameras) World Wide Web + Google (Google Book Search) manyWritingsPersonal online publishing / Web 2.0 (Blogs, Flickr, YouTube) Digital media (pc, video- cameras) World Wide Web + Google (Google Blog Search)

4 from WOW20 to SIGSNA: Working with online user generated content for Sociological Research WOW20 (2007)SIGSNA (2009) Social MediaBlogsFriendFeed Type of dataPublic RSS feed DatabaseRelational DB ExtrasScraping tecniquesLanguage identification Amount of data3000 blog entries10.454.195 FF post* * Entries and comments

5 The Big Data: New methodological problems -getting the data - storing the data - querying the data - analysing the data © Flickr.com / Southside Images

6 © Flickr.com / GeekMom Heather Many opportunities are with RSS feeds (content produced) or API (users info). Last.FM, Twitter, Flickr, Digg, Netlog, YouTube, MySpace… Contacts, status, profile, TopUsed…

7 © Flickr.com / GeekMom Heather - Legal/ethical issues - Terms of use

8 Storing the data: SIGSNA (two weeks of FriendFeed public data) ≃ 10.500.000 posts (2GB text data). ≃ 500.000 likes. ≃ 450.000 users. ≃ 15 million subscriptions. © Flickr.com / amanderson2

9 Summary:

10

11 examples: ≠ Heidi: 1974 Anime based on Johanna Spyri’s novel. Heidi: 1973 Top Model

12 Querying the data Case study: SIGSNA research on breaking news propagation on Friendfeed Mike Bongiorno (famous Italian TV host) died on Sept. 8 2010. The news stroke Friendfeed at 01.57 PM: - First entry >130 comments - All entries > 585 comments

13 How news propagate? What kind of behaviours?

14 Using timestamps and network of followers we have been able to track the propagation paths identifying major hubs.

15 Long propagation chains No propagation Short propagation chains

16 Explicit news sharing is followed by chatting and discussion. This kind of activity contribute to news propagation ” Bye Mike! We’re missing you! Bye granpa Mike! Mike, you’ve been a milestone of our TV “

17 First entry has the highest informative function Most commented entry is a long and articulated discussion

18 More info, papers and data: http://larica.uniurb.it/sigsna SIGSNA is a joint research project with the department of Computer Science of the University of Bologna (Dr. Matteo Magnani) and it is partially founded by Telecom Italia.


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