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Luca Rossi
Luca Rossi

Presentation done at the 4th research methods festival - July 5th 2010 - Oxford - St. Catrine College

Technology
1 of 22
SIGSNA:Special Interest Group on Social Network Analysis Luca Rossi - Fabio GigliettoUniversityof Urbino “Carlo BO”
Background:Growing availability of User Generated Content persistence/easy to search/scalability/ easy to replicate/(boyd 2007) High research value of spontaneously produced contents.
The Big (online) Data: New opportunities ,[object Object]
huge amount of spontaneous data
large variety of topics
worldwide phenomenon (comparative analysis),[object Object]
analysing the data} Interdisciplinaryapproach ©Flickr.com / SouthsideImages
Getting the data: RSS feeds (content produced) or API (users info). Last.FM, Twitter, Flickr, Digg, Netlog, YouTube, MySpace… Contacts, status, profile, TopUsed… ©Flickr.com / GeekMom Heather
- Legal/ethical issues - Terms of use ©Flickr.com / GeekMom Heather
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
from WOW20 to SIGSNA:Workingwith online usergeneratedcontentforSociologicalResearch
Summary:
data cleaning
examples: ≠ Heidi: 1973 Top Model Heidi: 1974 Anime based on JohannaSpyri’snovel.
Querying the data Case study: SIGSNA research on breaking news propagation on Friendfeed Mike Bongiorno (famousItalian TV host) died on Sept. 8 2010. The news stroke Friendfeed at 01.57 PM:- First entry >130 comments - Allentries > 585 comments
How news propagate?What kind of behaviours?
Using timestamps and network of followers we have been able to track the propagation paths identifying major hubs.
Short propagationchains Long propagationchains No propagation

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Information spreading in FriendFeed

  • 1. SIGSNA:Special Interest Group on Social Network Analysis Luca Rossi - Fabio GigliettoUniversityof Urbino “Carlo BO”
  • 2. Background:Growing availability of User Generated Content persistence/easy to search/scalability/ easy to replicate/(boyd 2007) High research value of spontaneously produced contents.
  • 3.
  • 4.
  • 5. huge amount of spontaneous data
  • 6. large variety of topics
  • 7.
  • 8. analysing the data} Interdisciplinaryapproach ©Flickr.com / SouthsideImages
  • 9. Getting the data: RSS feeds (content produced) or API (users info). Last.FM, Twitter, Flickr, Digg, Netlog, YouTube, MySpace… Contacts, status, profile, TopUsed… ©Flickr.com / GeekMom Heather
  • 10. - Legal/ethical issues - Terms of use ©Flickr.com / GeekMom Heather
  • 11. 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
  • 12. from WOW20 to SIGSNA:Workingwith online usergeneratedcontentforSociologicalResearch
  • 15. examples: ≠ Heidi: 1973 Top Model Heidi: 1974 Anime based on JohannaSpyri’snovel.
  • 16. Querying the data Case study: SIGSNA research on breaking news propagation on Friendfeed Mike Bongiorno (famousItalian TV host) died on Sept. 8 2010. The news stroke Friendfeed at 01.57 PM:- First entry >130 comments - Allentries > 585 comments
  • 17. How news propagate?What kind of behaviours?
  • 18. Using timestamps and network of followers we have been able to track the propagation paths identifying major hubs.
  • 19. Short propagationchains Long propagationchains No propagation
  • 20.
  • 21.
  • 22.
  • 23. Explicit news sharing is followed by chatting and discussion. This kind of activity contribute to news propagation “ Bye Mike! We’re missingyou!Bye granpa Mike! Mike, you’ve been a milestoneofour TV ”
  • 24. First entry has the highest informative function Most commented entry is a long and articulated discussion
  • 25. 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. MatteoMagnani) and it is partially founded by Telecom Italia.