Information propagationanalysis in a social

network siteMatteo Magnani* - Danilo Montesi* - Luca Rossi°

http://larica.uniurb.it/sigsna

* University of Bologna,Dept. of Computer

Science

° University of Urbino “Carlo Bo”,

Dept. of Communication Studies

Information propagationSocial Network Sites: ability to spread

information.

Pros and cons: we lose the context.

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.

•Epidemiology.•Computer viruses.•Random network models.•...

Propagation in a social contextNodes are people.The case of news propagation about the

death of a public figure (more details in two weeks @Socialcom).Explicit news propagation.Implicit news propagation through chatting.Mourning ritual of the networked public.How has

television changed?

Why do we call Mike grandpa while we don’t care about our biological grandfathers?

Bye Mike! We’re missing you!

Bye granpa Mike!

Bye Mike, you’ve been a milestone of our TV.

Are we all a bunch of hypocrites mourning for afamous old man who died while thousand of peopledie everyday in the world?

Mike passed away!Mike passed

away!

Propagation in a socio-technical context

Friendfeed

Friendfeed

Friendfeed

Data extraction

≃ 10.500.000 posts   ≃ 500.000 likes.   ≃ 450.000 users.  ≃ 15.000.000 di archi (subs).

Downloadable from: http://larica.uniurb.it/sigsna/data/

Static and dynamic networkIn other disciplines reconstructing the

network is complex. Here we get network structures for free.

However, the network over which information propagates is very different from the technical network.

High priority networksTeN HPN

Edges 14,946,610

161,603

Followers (avg)

44* 13*

* for users with public connections

Number of (on line) users v

isib

ilit

y

many online users = comments/likesmany online users = new content produced

Content production (IT)

Daily propagation

Hourly propagation

Entries/comments received

Entries/comments received (zoom)

Sources of entries

Source of information

avg 0,04 1,07 0,02 0,04 0,05

min 0 0 0 0 0

max 40 669 19 34 21st.dev

0,47 6,34 0,32 0,56 0,44

Multimediaentries 686,491comments 297,064avg 0.43

Global dataentries 9,107,217comments 1,346,978avg 0.15

Italian dataentries 10,729comments 13,202avg 1.23

.15

.43

1.23

Impact of Multimedia content

Language Fidelity Index

Language Fidelity Index: Number of Posts in a Language / Number of Posts of users with an entry in that language.

Cultural Influence

Time related to some eventsChat: depends on topic more than time.News: the winner takes all.

News Chat

7 top commented threads about Mike’s death

Duration vs Comments

Research findings 1/2Users active inside Friendfeed generate much more

comments than external users importing their messages into the service.

Content production rate follows specific time-trends.The average audience of an entry depends on its

posting time with specifically identified trends.Information spreads on High Priority Networks built

on top of the technical network.Automated users tend not to generate discussions. The number of comments received by users with more

limited entry production rates increases only up to some threshold (information overload).

Most conversations have a very quick growth and an evolution that usually ends within a few hours.

This is particularly evident for highly commented entries —the presence of many comments often implies a shorter discussion.

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.

This does not seem to apply to the second kind of messages, which may start days after the news occurred.

Research findings 2/2

Moral

Identification of some of these factors (source, multimedia, culture, timing, kind of message, active network).

Quantitative analysis on a real dataset.

The “success” of a post depends on many factors

related to its socio-technical context .

Information propagationanalysis in a social

network siteMatteo Magnani* - Danilo Montesi* - Luca Rossi°

http://larica.uniurb.it/sigsna

* University of Bologna,Dept. of Computer

Science

° University of Urbino “Carlo Bo.”

Dept. of Communication Studies

SIGSNA project (google).

Twitter:sigsnamatmagnanilrossi