Friendfeed breaking news: death of a public figure

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Slides used at 2010 SOCIALCOM conference: social and technical factors enabling propagation of breaking news in social network sites

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  • Conversations AND Many cultures.
  • Friendfeed breaking news: death of a public figure

    1. 1. Friendfeed breaking news:death of a public figure<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. Introduction<br />Social Network Sites: ability to spread information.<br />How does breaking news propagate in a SNS?<br />An empirical analysis of breaking news propagation on a real SNS to identify socio-technical patterns.<br />Choice of a SNS.<br />Monitoring.<br />Extraction of posts related to some breaking news.<br />Identification and interpretation of propagation patterns.<br />
    3. 3. Friendfeed<br />
    4. 4. SNS monitoring<br />≃ 10.500.000 posts   ≃ 500.000 likes.   ≃ 450.000 users.  ≃ 15.000.000 edges (subscriptions).<br />Downloadable from: http://larica.uniurb.it/sigsna/data/<br />
    5. 5. In this talk: death of a public figure<br />The news stroke Friendfeed users at 01.57 PM, Sep. 8.<br />At that time only SkyTG24 was broadcasting the event.<br />At the end of the day the death of Mike Bongiorno counted 585 comments, 276 during the first hour.<br />
    6. 6. Data pre-processing<br />10,456,233<br />Language filter<br />Keyword filter<br />196,350<br />Cleaning<br />939<br />Discussion reconstruction<br />936<br />1,473<br />
    7. 7. Propagation in a social context<br />Three patterns identified through a qualitative analysis of the posts.<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 />
    8. 8. Role of mourning ritual<br />
    9. 9. Following structured conversations<br />Chat: depends on topic more than time.<br />News: the winner takes all.<br />Chat<br />News (FE, 2nd top commented)<br />7 top commented threads about Mike’s death<br />
    10. 10. Following distributed propagations<br />
    11. 11. Propagation model<br />
    12. 12.
    13. 13.
    14. 14.
    15. 15. Research findings 1/2<br />Breaking news about the death of a public figure propagated through three main kinds of discussions: those giving the news, those expanding related topics, and R.I.P..<br />The first kind of discussion may evolve into the second.<br />Their life cycles are significantly different.<br />The first has a peak which decreases after short time. <br />The second, made of longer messages, may stay alive longer, keeping the news active on the SNS.<br />The third tends not to produce interactions.<br />This is a direct consequence of the different social roles of these conversations.<br />
    16. 16. Both kinds of message (first and second) may generate a high number of comments.<br />For news messages time is relevant.<br />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 chats, which may start days after the news occurred.<br />The large majority of messages exchanged on the topic originates, directly or indirectly, from a single message (FE, in our case study).<br />Messages inoculated by automated services may reach a large number of users directly following them, but:<br />They do not generate comments.<br />It appears that the majority of those users already learned the news.<br />Research findings 2/2<br />
    17. 17. Main message<br />The propagation of breaking news follows patterns that can be understood only by considering the specific socio-technical features of the medium.<br />
    18. 18. QUESTIONS?<br />
    19. 19. Friendfeed breaking news:death of a public figure<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 />Google: SIGSNA<br />Twitter: <br />sigsna<br />matmagnani<br />lrossi<br />http://larica.uniurb.it/sigsna<br />
    20. 20. 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 />
    21. 21. Death of Patrick Swayze:<br />interaction network<br />

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