morning sessions at event today on social nets run by Assyst

This event: http://www.cnn.group.cam.ac.uk/news/scientific-meeting-on-social-networks-and-social-media-18th-january

Organised by: http://www.assystcomplexity.eu/
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Bernardo Huberman (HP)  - Getting Attention1. In last decade, web moved from download to 2-way2, Everyone has a megaphone (the interweb is one big ethernet?:-)3. attention is therefore the scarce resource attention is a coordination/rendezvous/synch mechanism anything except attention can be commodotized defn: attention in social terms (pagerank)4. Looking at this, we can build predictors and make money...5. Looked at Attention Seeking behaviours... doesn't matter if its your friends who applaud
Sanjeev Goyal - Contagion1. random v. smart attacks2. central v. distributed Net designer3. Design for resilience
Q (BH). can we filter spam on this basis/cost?(c.f. Ross work - can't afford email anymore) ------>
Maxi San Miguel  (IFISC) What can we learn from simple social behaviour models
isolate interaction mechanisms and find collective effectsfind causal relationsips...
one example: random imitation role of topology co-evolution heterogeneity in timing of interactionsExample: voters - imitation dynamics abosrbing states (all coloured red/green) when&how do we get (what about metastable case/oscillitory?) results anayltical for regular nets show ordering... in complex nets (small world or BA etc) , can get  non stable soln long range ties don't ctually help reachign agreement, counter-intuitive critical beaviour determined by mean node degree (c.f. haggle)dynamics of net (formation) & on the net (usage) but can we have co-evolution of agents and netrightwing view is net determis individual chocesleftwin view is individals constrined by social net :-) co-evol has model of agent/link changing/selected with distribution re-wiring rate v. use rate has critical phase changesheterogeneity in timing of individual activitie... empirically, we don't work at constant rate... so include a notion of node-specific internal  time..
Keys dimenaionality coevolution timingTakehomes strong messages dont homogenize, but polarize social interaction can lead to consenss different from external message
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Martin Everett - Manchester - dual project for 2 mode data
2 distinct groups - interaction netween group, not users in each group e.g. wikipedia & postersexample dataset women & southern events...problem - projection can lose data can we do directly? not necessarily...turns out (math says) there isn't much loss... can do SVD  makes it easy to find core/key eents,  and key people in women/southern fried chicken e.g. and various other clustering things...can use this to find centrality- discover key agents in women getting other women to events
takehome -  ignore incorrect folkore about losing info in this approach...

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Ross Anderson (CL) Temporal Node Centrality - work with hyoungschick kim

 

starting with attacks/defense on scale free net hubs etc....

 

results in papers by Hyoungschick et a...

 

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Cecilia Mascolo (CL) Geo-spatial

Case study - add in geo-info (like foursquare) - with geo-tagging of postsm can see where messages/interactions occur.

 

questions of interest -

1. relation twixt geo-distance and social distance - for example

2. distance and degree

Applications - can we exploit geo-spatial info to build better social apps and systems?

1. link prediction....

2. movement model/prediction

Q&A for morning session chaired by Y.T. (for readers of snow crash:)

 

Someone more sociable than I can blog the pm:)

Our Twitter Profiles, Our Selves

I presented a couple of papers at this year's Socialcom . While I was presenting, the twittersphere was offering encouraging and puzzling feedbacks:

• I love the way @danielequercia introduces a book to read in each of his talk :D
• I really like @danielequercia style in making slides and presenting! minimal, cool and fun :D

The irony is that, during the  coffee break right before my talk, I  received few constructive feedbacks on how to structure my presentations and avoid having, as I often do,  superficial and high-level slides for a *scientific* talk. Well, that's not the first time I get this feedback, and I accept it. However, I feel that many talks at conferences suffer from powerpoint karaoke syndrome - to look "right" (like a proper scientist/professional dude), one needs to recast a paper into slide format. Bad mistake, as The Great Simon L Peyton Jones would tell us. Since I apparently like to suggest books, then let me say that, despite the title, "Presenting to Win" is the best book on how to prepare and deliver presentations (it's for a business audience, but you can easily adapt it to your needs).  Ideally, one should be able to give a talk without any slide - this way, i bet that karaoke presenters will be more likely to reach enlightenment and enter nirvana (provided that they spend 3 days to prepare a 15-minute presentation). If a smooth transition between powerpoint karaoke and nirvana is needed, then  karaoke presenters  might well try the "Takahashi Method"  -  Lawrence Lessig has successfully used it (link to one of his talks) and Steve Jobs was doing something similar for his keynotes.

Anyhooow :)  this post isn't about presentation styles but about the two papers I presented :) Here is a quick abstract that summarizes them. Enjoy ;)

In the first paper [pdf paper slides], we tested whether Twitter users can be reduced to look-alike nodes (as most of the spreading models would assume) or, instead, whether they show individual differences that impact their popularity and influence. One aspect that may differentiate users is their character and personality. The problem is that personality is difficult to observe and quantify on Twitter. It has been shown, however, that personality is linked to what is unobtrusively observable in tweets: the use of language. We thus carry out a study of tweets and show that popular and influential users linguistically structure their tweets in specific ways. This suggests that the popularity and influence of a Twitter account cannot be simply traced back to the graph properties of the network within which it is embedded, but also depends on the personality and emotions of the human being behind it. Also, in the second paper [pdf paper slides], for a limited number of 335 users, we are able to gather personality data, analyze it, and find that both popular users and influentials are extroverts and emotionally stable (low in the trait of Neuroticism). Interestingly, we also find that popular users are "imaginative" (high in Openness), while influentials tend to be "organised" (high in Conscientiousness). We then show a way of accurately predicting a user's personality simply based on three counts publicly available on profiles: following, followers, and listed counts. Knowing these three quantities about an active user, one can predict the user's five personality traits with a root- mean-squared error below 0.88 on a [1,5] scale. Based on these promising results, we argue that being able to predict user personality goes well beyond our initial goal of informing the design of new personalized applications as it, for example, expands current studies on privacy in social media.