1. Information Propagation and Extraction
from Twitter
Dr. Matteo Magnani – University of Bologna
Dr. Luca Rossi – University of Urbino Carlo Bo
http://larica.uniurb.it/sigsna

2. Focus of the talk: INTERDISCIPLINARITY
Computer scientist Sociologist

3. The Computer Scientist's perspective

4. The Social Scientist's perspective

5. The Social Scientist's perspective

6. Outline
● Extracting information from Twitter.
● Studying information propagation on Twitter.
● Beyond Twitter: multi-layer networks.

7. From Tweet retrieval…
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8. From Tweet retrieval… to Conversation Retrieval
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9. Conversation Retrieval System
Some basic problems:
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Off-line and on-line conversations are different.
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What is a conversation on Twitter?
Ranking parameters:
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text relevance
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popularity of users
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popularity of messages
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timeliness
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non-verbal signals
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(style, emoticons, density)

10. Conversation Retrieval System Architecture

11. (Demo of the system – on line)

12. Evaluation
Topic: Chilean mining accident
user evaluation of:
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Google search (a),
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Conversation Retrieval with high popularity (b),
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Conversation Retrieval with high density (c)
(x: score, y: number of votes)
(a) (b) (c)

13. Evaluation
Topic: Death of former Italian President (Francesco Cossiga)
user evaluation of:
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Google search (a),
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Conversation Retrieval with high popularity (b),
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Conversation Retrieval with high density (c)
(x: score, y: number of votes)
(a) (b) (c)

14. Information propagation: epidemiological model

15. Information propagation: epidemiological model

16. Information propagation: epidemiological model

17. Information propagation in a socio-technical context
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Are we sure people have been exposed?
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What about information persistence?
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Users may (explicitely or implicitly) decide to propagate information.
TWO STEPS:
1) Identify propagation paths
2) Interpret the results -> patterns
Older posts

18. TWO STEPS:
1) Identify propagation paths
2) Interpret the results -> patterns
Case study: Mike Bongiorno's death (Italian TV Anchorman)

19. TWO STEPS:
1) Identify propagation paths
2) Interpret the results -> patterns
Case study: Mike Bongiorno's death (Italian TV Anchorman)

20. TWO STEPS:
1) Identify propagation paths
2) Interpret the results -> patterns
Case study: Mike Bongiorno's death (Italian TV Anchorman)

21. TWO STEPS: Mike passed away!
1) Identify propagation paths
2) Interpret the results patterns How has television
changed?
Bye granpa Mike!
Case study: Mike Bongiorno's death (Italian TV Anchorman)

22. 7 top commented threads about Mike’s death
Case study: Mike Bongiorno's death (Italian TV Anchorman)

23. Rescue operations for 33 Chilean Miners (Oct. 2010)
Global breaking news, data collected on Twitter and FriendFeed
Twitter conversation on the Miners’s
rescue. It is possible to see how local
national communities still exist.

24. Users belong to several networks at the same time!
Knowledge of other networks is essential also to study
internal Twitter dynamics.

25. ML model for multi-networks
User C: Degree dentrality User C: Degree dentrality
3 (net 1) and 3 (net 2) 2 (net 1) and 2 (net 2)
However, in the second (RHS) system user C is connected to more people!

26. Information Propagation and Extraction
from Twitter
Dr. Matteo Magnani – University of Bologna
Dr. Luca Rossi – University of Urbino Carlo Bo
http://larica.uniurb.it/sigsna