SIGSNA:Special Interest Group on Social Network Analysis<br />Luca Rossi - Fabio GigliettoUniversityof Urbino “Carlo BO”<b...
Background:Growing availability of User Generated Content<br />persistence/easy to search/scalability/ easy to replicate/(...
The Big (online) Data: New opportunities<br /><ul><li> high value of UGCs
 huge amount of spontaneous data
 large variety of topics
 worldwide phenomenon (comparative analysis)</li></li></ul><li>The Big Data: New methodological problems<br /><ul><li> get...
 analysing the data</li></ul>}<br />Interdisciplinaryapproach<br />©Flickr.com / SouthsideImages<br />
Getting the data:<br />RSS feeds (content produced) or API (users info). <br />Last.FM, Twitter, Flickr, Digg, Netlog, You...
- Legal/ethical issues<br />- Terms of use <br />©Flickr.com / GeekMom Heather <br />
Storing the data:<br />SIGSNA (two weeks of FriendFeed public data)≃10.500.000 posts (2GB text data). ≃ 500.000 likes.  ≃ ...
from WOW20 to SIGSNA:Workingwith online usergeneratedcontentforSociologicalResearch<br />
Summary:<br />
data cleaning<br />
examples:<br />≠<br />Heidi: 1973 Top Model<br />Heidi: 1974 Anime based on JohannaSpyri’snovel.<br />
Querying the data <br />Case study: SIGSNA research on breaking news propagation on Friendfeed<br />Mike Bongiorno (famous...
How news propagate?What kind of behaviours?<br />
Using timestamps and network of followers we have been able to track the  propagation paths identifying major hubs.<br />
Short propagationchains<br />Long propagationchains<br />No propagation<br />
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Information spreading in FriendFeed

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

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Information spreading in FriendFeed

  1. 1. SIGSNA:Special Interest Group on Social Network Analysis<br />Luca Rossi - Fabio GigliettoUniversityof Urbino “Carlo BO”<br />
  2. 2. Background:Growing availability of User Generated Content<br />persistence/easy to search/scalability/ easy to replicate/(boyd 2007)<br />High research value of spontaneously produced contents.<br />
  3. 3.
  4. 4. The Big (online) Data: New opportunities<br /><ul><li> high value of UGCs
  5. 5. huge amount of spontaneous data
  6. 6. large variety of topics
  7. 7. worldwide phenomenon (comparative analysis)</li></li></ul><li>The Big Data: New methodological problems<br /><ul><li> getting the data- storing the data - querying the data
  8. 8. analysing the data</li></ul>}<br />Interdisciplinaryapproach<br />©Flickr.com / SouthsideImages<br />
  9. 9. Getting the data:<br />RSS feeds (content produced) or API (users info). <br />Last.FM, Twitter, Flickr, Digg, Netlog, YouTube, MySpace…<br />Contacts, status, profile, TopUsed…<br />©Flickr.com / GeekMom Heather <br />
  10. 10. - Legal/ethical issues<br />- Terms of use <br />©Flickr.com / GeekMom Heather <br />
  11. 11. Storing the data:<br />SIGSNA (two weeks of FriendFeed public data)≃10.500.000 posts (2GB text data). ≃ 500.000 likes.  ≃ 450.000 users. ≃ 15 million subscriptions.   <br />©Flickr.com / amanderson2<br />
  12. 12. from WOW20 to SIGSNA:Workingwith online usergeneratedcontentforSociologicalResearch<br />
  13. 13. Summary:<br />
  14. 14. data cleaning<br />
  15. 15. examples:<br />≠<br />Heidi: 1973 Top Model<br />Heidi: 1974 Anime based on JohannaSpyri’snovel.<br />
  16. 16. Querying the data <br />Case study: SIGSNA research on breaking news propagation on Friendfeed<br />Mike Bongiorno (famousItalian TV host) died on Sept. 8 2010. The news stroke Friendfeed at 01.57 PM:- First entry >130 comments<br />- Allentries > 585 comments<br />
  17. 17. How news propagate?What kind of behaviours?<br />
  18. 18. Using timestamps and network of followers we have been able to track the propagation paths identifying major hubs.<br />
  19. 19. Short propagationchains<br />Long propagationchains<br />No propagation<br />
  20. 20.
  21. 21.
  22. 22.
  23. 23. Explicit news sharing is followed by chatting and discussion. This kind of activity contribute to news propagation<br />“<br />Bye Mike! We’re missingyou!Bye granpa Mike!<br />Mike, you’ve been a milestoneofour TV<br />”<br />
  24. 24. First entry has the highest informative function<br />Most commented entry is a long and articulated discussion<br />
  25. 25. More info, papers and data:<br />http://larica.uniurb.it/sigsna<br />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.<br />
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