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 />
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.
...</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 />
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 />
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 produced<br />
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 />
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 />
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 />
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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