Information propagationanalysis in a socialnetwork site<br />Matteo Magnani* - Danilo Montesi* - Luca Rossi° <br />* Unive...
Information propagation<br />Social Network Sites: ability to spread information.<br />Pros and cons: we lose the context....
Computer viruses.
Random network models.
...</li></li></ul><li>Propagation in a social context<br />Nodes are people.<br />The case of news propagation about the d...
Propagation in a socio-technical context<br />
Friendfeed<br />
Friendfeed<br />
Friendfeed<br />
Data extraction<br />≃ 10.500.000 posts   ≃ 500.000 likes.   ≃ 450.000 users.  ≃ 15.000.000 di archi (subs).<br />Download...
Static and dynamicnetwork<br />In otherdisciplinesreconstructing the network is complex. Herewegetnetworkstructures for fr...
High priority networks<br />* for users with public connections<br />
Number of (on line) users<br />many online users = comments/likes<br /> visibility<br />many online users = new content pr...
Content production (IT)<br />
Daily propagation<br />
Hourly propagation<br />
Entries/comments received<br />
Entries/comments received (zoom)<br />
Sources of entries<br />
avg<br />0,04<br />1,07<br />0,02<br />0,04<br />0,05<br />min<br />0<br />0<br />0<br />0<br />0<br />max<br />40<br />66...
Impact of Multimedia content<br />.15<br />.43<br />1.23<br />
Language Fidelity Index<br />Language Fidelity Index: Number of Posts in a Language / Number of Posts of users with an ent...
Cultural Influence<br />
Time related to some events<br />Chat: depends on topic more than time.<br />News: the winner takes all.<br />Chat<br />Ne...
Duration vs Comments<br />
Research findings 1/2<br />Users active inside Friendfeed generate much more comments than external users importing their ...
Most conversations have a very quick growth and an evolution that usually ends within a few hours.<br />This is particular...
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Information propagation in a social network site

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An empirical analysis of the socio-technical factors influencing information propagation in conversational social network sites

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Information propagation in a social network site

  1. 1. Information propagationanalysis in a socialnetwork site<br />Matteo Magnani* - Danilo Montesi* - Luca Rossi° <br />* University of Bologna,<br />Dept. of Computer Science<br />° University of Urbino “Carlo Bo”,<br />Dept. of Communication Studies<br />http://larica.uniurb.it/sigsna<br />
  2. 2. Information propagation<br />Social Network Sites: ability to spread information.<br />Pros and cons: we lose the context.<br />In this (and related) work we try to measure the impact of specific aspects of SNSs on information propagation through the analysis of real data.<br /><ul><li>Epidemiology.
  3. 3. Computer viruses.
  4. 4. Random network models.
  5. 5. ...</li></li></ul><li>Propagation in a social context<br />Nodes are people.<br />The case of news propagation about the death of a public figure (more details in two weeks @Socialcom).<br />Explicit news propagation.<br />Implicit news propagation through chatting.<br />Mourning ritual of the networked public.<br />Mike passed away!<br />How has television changed?<br />Mike passed away!<br />Bye Mike! We’re missing you!<br />Bye granpa Mike!<br />Are we all a bunch of hypocrites mourning for a<br />famous old man who died while thousand of people<br />die everyday in the world?<br />Why do we call Mike grandpa while we don’t care about our biological grandfathers?<br />Bye Mike, you’ve been a milestone of our TV.<br />
  6. 6. Propagation in a socio-technical context<br />
  7. 7. Friendfeed<br />
  8. 8. Friendfeed<br />
  9. 9. Friendfeed<br />
  10. 10. Data extraction<br />≃ 10.500.000 posts   ≃ 500.000 likes.   ≃ 450.000 users.  ≃ 15.000.000 di archi (subs).<br />Downloadable from: http://larica.uniurb.it/sigsna/data/<br />
  11. 11. Static and dynamicnetwork<br />In otherdisciplinesreconstructing the network is complex. Herewegetnetworkstructures for free.<br />However, the network over which information propagates is verydifferent from the technicalnetwork.<br />
  12. 12. High priority networks<br />* for users with public connections<br />
  13. 13. Number of (on line) users<br />many online users = comments/likes<br /> visibility<br />many online users = new content produced<br />
  14. 14. Content production (IT)<br />
  15. 15. Daily propagation<br />
  16. 16. Hourly propagation<br />
  17. 17. Entries/comments received<br />
  18. 18. Entries/comments received (zoom)<br />
  19. 19. Sources of entries<br />
  20. 20. avg<br />0,04<br />1,07<br />0,02<br />0,04<br />0,05<br />min<br />0<br />0<br />0<br />0<br />0<br />max<br />40<br />669<br />19<br />34<br />21<br />st.dev<br />0,47<br />6,34<br />0,32<br />0,56<br />0,44<br />Source of information<br />
  21. 21. Impact of Multimedia content<br />.15<br />.43<br />1.23<br />
  22. 22. Language Fidelity Index<br />Language Fidelity Index: Number of Posts in a Language / Number of Posts of users with an entry in that language.<br />
  23. 23. Cultural Influence<br />
  24. 24. Time related to some events<br />Chat: depends on topic more than time.<br />News: the winner takes all.<br />Chat<br />News<br />7 top commented threads about Mike’s death<br />
  25. 25. Duration vs Comments<br />
  26. 26. Research findings 1/2<br />Users active inside Friendfeed generate much more comments than external users importing their messages into the service.<br />Content production rate follows specific time-trends.<br />The average audience of an entry depends on its posting time with specifically identified trends.<br />Information spreads on High Priority Networks built on top of the technical network.<br />Automated users tend not to generate discussions. <br />The number of comments received by users with more limited entry production rates increases only up to some threshold (information overload).<br />
  27. 27. Most conversations have a very quick growth and an evolution that usually ends within a few hours.<br />This is particularly evident for highly commented entries —the presence of many comments often implies a shorter discussion.<br />For informational messages, time is relevant. Given the high rate of answers, an early message may have a saturation effect so that it aggregates the majority of discussions and limits the development of conversations on other similar messages.<br />This does not seem to apply to the second kind of messages, which may start days after the news occurred.<br />Research findings 2/2<br />
  28. 28. Moral<br />Identification of some of these factors (source, multimedia, culture, timing, kind of message, active network).<br />Quantitative analysis on a real dataset.<br />The “success” of a post depends on many factors related to its socio-technical context .<br />
  29. 29. SIGSNA project (google).<br />Twitter:<br />sigsna<br />matmagnani<br />lrossi<br />Information propagationanalysis in a socialnetwork site<br />Matteo Magnani* - Danilo Montesi* - Luca Rossi° <br />* University of Bologna,<br />Dept. of Computer Science<br />° University of Urbino “Carlo Bo.”<br />Dept. of Communication Studies<br />http://larica.uniurb.it/sigsna<br />

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