syslog
2Oct/132

Liveblogging the first Human Data Interaction workshop

Posted by Anil Madhavapeddy

I'm at the Open Data institute, with Richard Mortier, Jon Crowcroft, Amir Chaudhry and Hamed Haddadi , live-blogging a daylong workshop about our emerging Human-Data Interaction research initiative.  The room is packed with notable researchers from all over the UK, so this promises to be an exciting day!

15Jul/122

Raspberry Jam – Bastille Day

Posted by Jon Crowcroft

The glorious quatorze juillet in the Computer lab, we had >250 people in attendance at the Raspberry Jam, and TeachMeet, to bring toegether raspberry owners, wannabes, hackers and observers, and then to discuss specifics (e.g. lesson plans) for using Rasberry Pis and related tech for teaching the Computing at School curriculum.

 

For me, highlights included

1. Demo of RiscOS on Pi

2. School governer showing how to democratize ICT/CS in the school by embedding it in everything (using free and/or opensource s/w only, and no geek/operator/ICT technicians at all)

 

3. A teaching who created over a dozen Digital Leaders to teach computing out of her own 12 year old pupils - these kids to stand up classes and tutorials for parents - just awesome.

 

4. two talks on literally hundreds of projects out there to carry out in D&T or other non-directly CS classes

 

5. How to teach healthcare through computers

6. Plenty of hints on first steps in programming

7. Finally Pi foundation folks showed up with 200 devices for people at the event. Also announced various new things (e.g. camera board should be ready Real Soon Now)...

 

A lot of fun, I thought. Judge for yourself from the video:

part 1
and
part 2

 

One of Leon's vid of the Zoo talk - excelent!

18Jan/120

morning sessions at event today on social nets run by Assyst

Posted by Jon Crowcroft

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:)

Filed under: Conference, Social No Comments
21Oct/110

NetMob 2011

Posted by Salvatore Scellato

Last week I was in (the other) Cambridge, attending the "Second conference on the Analysis of Mobile Phone Datasets and Networks", or NetMob, held at the MIT Media Lab together with SocialCom 2011. NetMob provides an interesting format: there is only one track of short contributed talks, with the possibility to present recent results or results submitted elsewhere.  Speakers have about 10-12 minutes to present their work and then there is plenty of time to discuss ideas network with other people over 2 days. I gave two talks: one of our research on the effect of geographic distance on online social networks and another on our recent work on universal patterns in urban human mobility.

The unifying theme of the workshop is the analysis of mobile phone datasets: as people user mobile devices more and to do more things, these datasets help us to understand complex processes such as spread of information, human mobility, the usage of urban geography and so on. Indeed, the range of talks presented at the workshop was impressive and fascinating, spanning between two main points: the first day focused more on studying user mobility, while the second day featured works on social behaviour.

Among the most innovative works during the first day there was a talk by people at MIT & Berkeley on using mobile phone CDRs to make sense of urban roads, proposing to use a the Gini coefficient to measure the diversity of individual traffic carried by each street. Individual user mobility was the main theme of several talks: I particularly liked one on the seasonal patterns of user movements, presented by Northeastern University researchers, and one by a large team led by Vincent Blondel on exploring the spatio-temporal properties of human mobility and the regular home-work routine of many users. Laszlo Barabasi gave an invited talk on mobility and predictability, presenting much of his last work and trying to connect the statistical properties of human mobility to the performance limits of many related applications that rely on user regularity. Finally, AT&T Labs presented their results on why it is impossible to anonymize location data.

The second day featured works on the social properties of mobile phone communication between users. Researchers at CMU presented their results on quantifying how social influence might compel users to adopt some products by using randomization techniques. Another interesting talk by a a joint team UC3M and Telefonica presented how time allocation in social networks has strong constraint that are likely to affect and be affect by the social structure itself: well-connected hubs have a lower importance on information transmission than less connected users, with important consequences on many dynamic social processes. Sandy Pentland have another invited talk, offering a wide overview of how mobile devices are changing the technological landscape with their ubiquitous sensing capabilities. Another interesting talk discussed the economic value of mobile location data, presenting scenarios user actions can be monetized and profit shared among different service providers.

Overall NetMob provided an insightful venue for discussions and potential collaborations, always revolving around the idea that as mobile devices become more and more ubiquitous they will offer new fascinating research opportunities.

Many more details about all the talks in the book of abstracts.

 

20Oct/110

Our Twitter Profiles, Our Selves

Posted by Daniele Quercia

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.

Filed under: Conference, Social No Comments