twitter information extraction and propagation: an interdisciplinary view
TRANSCRIPT
Information Propagation and Extractionfrom Twitter
Dr. Matteo Magnani – University of BolognaDr. Luca Rossi – University of Urbino Carlo Bo
http://larica.uniurb.it/sigsna
Computer scientist Sociologist
Focus of the talk: INTERDISCIPLINARITY
The Computer Scientist's perspective
The Social Scientist's perspective
The Social Scientist's perspective
Outline
● Extracting information from Twitter.● Studying information propagation on Twitter.● Beyond Twitter: multi-layer networks.
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From Tweet retrieval…
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From Tweet retrieval… to Conversation Retrieval
Some basic problems: ●Off-line and on-line conversations are different.● What is a conversation on Twitter?
Ranking parameters: ● text relevance● popularity of users● popularity of messages● timeliness● non-verbal signals
● (style, emoticons, density)
Conversation Retrieval System
Conversation Retrieval System Architecture
(Demo of the system – on line)
Topic: Chilean mining accidentuser evaluation of:● Google search (a),● Conversation Retrieval with high popularity (b),● Conversation Retrieval with high density (c)(x: score, y: number of votes)
Evaluation
(c)(b)(a)
(c)(b)(a)
Topic: Death of former Italian President (Francesco Cossiga)user evaluation of:● Google search (a),● Conversation Retrieval with high popularity (b),● Conversation Retrieval with high density (c)(x: score, y: number of votes)
Evaluation
Information propagation: epidemiological model
Information propagation: epidemiological model
Information propagation: epidemiological model
Older posts
Information propagation in a socio-technical context
● Are we sure people have been exposed?● What about information persistence?● Users may (explicitely or implicitly) decide to propagate information.
TWO STEPS:1) Identify propagation paths2) Interpret the results -> patterns
TWO STEPS:1) Identify propagation paths2) Interpret the results -> patterns
Case study: Mike Bongiorno's death (Italian TV Anchorman)
TWO STEPS:1) Identify propagation paths2) Interpret the results -> patterns
Case study: Mike Bongiorno's death (Italian TV Anchorman)
TWO STEPS:1) Identify propagation paths2) Interpret the results -> patterns
Case study: Mike Bongiorno's death (Italian TV Anchorman)
How has television changed?
Mike passed away!
Bye granpa Mike!
TWO STEPS:1) Identify propagation paths2) Interpret the results � patterns
Case study: Mike Bongiorno's death (Italian TV Anchorman)
7 top commented threads about Mike’s death
Case study: Mike Bongiorno's death (Italian TV Anchorman)
Twitter conversation on the Miners’s rescue. It is possible to see how local national communities still exist.
Rescue operations for 33 Chilean Miners (Oct. 2010)
Global breaking news, data collected on Twitter and FriendFeed
Users belong to several networks at the same time!
Knowledge of other networks is essential also to study internal Twitter dynamics.
ML model for multi-networks
User C: Degree dentrality 3 (net 1) and 3 (net 2)
User C: Degree dentrality 2 (net 1) and 2 (net 2)
However, in the second (RHS) system user C is connected to more people!
Information Propagation and Extractionfrom Twitter
Dr. Matteo Magnani – University of BolognaDr. Luca Rossi – University of Urbino Carlo Bo
http://larica.uniurb.it/sigsna