here are few papers presented at socialcom (our two papers on personality and language are summarized here)
Funf: Open Android Sensing Framework. One tutorial at socialcom was dedicated to Funf. This is an open source set of functionalities running on phones and servers that enable the collection (sensing), uploading, and configuration of a wide range of data types (location, movement, usage, social proximity). This framework has been built by a professional developer within Sandy Pentland's group (thanks to a Google grant), and has just been made publicly available on the android market (well done!) (download link ). The conference featured a considerable number of papers that made use of the framework. A case in point is [1]. This paper is about predicting "who installs which (mobile) app" based on one's social network (here the term network refers to a composite graph made of different types of phone-sensed networks). It turns out that one has more common apps with familiar strangers than with friends (i'm not 100% sure though, you need to check the paper). A cute bit of the framework is its fun dashboard - this allows researchers to run studies in which personal data is shown to the participants and consequential changes of behaviour can be automatically traced. The ubicomp paper [2] highlights the vision behind the framework.
[1] Composite Social Network for Predicting Mobile Apps Installation
[2] the social fMRI: Measuring, Understanding and Designing Social Mechanism in the Real World. Ubicomp 11.
Another "special" session was dedicated to cyber-bullying - an extremely interesting topic in need of research (pdf overview). Folks at the media lab built an initial model to spot cyber-bullying from conversation in social media. Interestingly, they trained the model using the data from this site. The paper will soon be published and will be titled "Commonsense Reasoning for Detection, Prevention, and Mitigation of Cyberbullying"
Predicting Reciprocity in Social Networks. This paper studied the factors that are associated with the probability that a node w reciprocates and links to a node v in a social network. The most important factor is the difference in status between the two nodes v and w: status(v)/status(w), where status(v)=in_degree(v)/out_degree(v).
The larger that fraction, the more likely w will reciprocates the link. That is because a large denominator and small numerator indicate that v has many in-links and few out-links and that w has many out-links and few in-links. This suggests that v has higher "status" than w will be more likely to reciprocate.
Link Prediction in Social Networks using Computationally Efficient Topological Features. Using katz measure, these researchers effectively predicted social ties in a variety of networks. This isn't a very novel work, yet it's interesting that Katz measure performed best.
The new director of the media lab, Joi Ito, gave a interesting keynote on "Open Standards and Open Networks". He recounted his involvement in a post-disaster radiation monitoring effort in Japan. During his talk, I also learned that the a large number of governments are realising their data (not pictures or videos, but data) under creative common licence.
Fortune Monitor or Fortune Teller: Understanding the Connection between Interaction Patterns and Financial Status. This paper studied the relationship between interactions monitored using mobile phones and financial status. Apparently people with high income don't talk longer but their meeting patterns (mobility) tend to be more diverse than those of people on low income. They also studied people's personality traits and found that people high in
1) Agreeableness tend to have more friends and interact with diverse users (as per face-to-face interactions monitored with bluetooth)
2) Happiness [i hope they measured satisfaction with life] tend to be more diverse contact (it would be cool to double check the measure of diversity used here)
The workshop NetMob was running in parallel and featured a lot of interesting talks that used mobile phone data to answer very interesting societal questions. The full program is in pdf. Salvo fully attended it, so he might be able to tell you more about it ;)
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 :) Â 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.
till tomorrow, i'm at the web science summer school. i was invited to give a talk on privacy in mobile-social networking applications. my talk was a re-mix of blog posts and papers (including spotme, "what we geeks don’t get about social media privacy", and "location-related privacy in geo-social networks" - pdf ). unfortunately i could not attend the whole summer school, but you can check here the schedule and my notes on a couple of talks are next.
marcel karnstedt gave a great presentation on the effects of user features on churn in social networks. he presented a nice empirical study of the mechanisms by which a web forum maintains a viable user base. he found that different forums show different behavioural patterns and also found few interesting regularities. have a go at his paper (pdf)
bernie hogan wondered what kind of mental models people have of their Facebook personal (ego) networks. to answer this question, he collected mental models that a number of Facebook users have about their personal networks, collected the actual personal networks from Facebook, clustered them using a community detection algorithm, and looked at the extent to which mental maps overlapped with actual networks. he found that people are good at identifying the clusters they are involved in but are not good at identifying which of their social contacts act as `brokers' in the network. this finding has interesting implications - eg, since opportunities/new ideas tend to come from brokers and people find it difficult to identify brokers, then it follows that people do not know where to look for new ideas, right? ;) bernie also said that neurotics tend to have broken networks, while extroverts tend to have clustered networks. check bernie's publications here!
the student projects look very interesting. they include collaborative filtering, sentiment analysis, and community detection.
few days ago, i attended the main social networks conference/gathering in UK.
there was an interesting discussion about the future of the elsevir journal "social networks". apparently, if you want to have an easy time getting in, you need to do research on 'methodology'. frankly, IMHO, this is the best thing they could do to kill the journal. alas, the journal's table of contents already reflect this decision. that is why i have rarely found interesting articles in this journal, while first monday and AJS are full of great contributions. don't get me wrong. i love methodological contributions to social networks - tom snijders and sinan aral are doing fantastic work in this area. i just think that methodological contributions are only a tiny part of a larger picture, a picture that hosts amazing work by, eg, duncan watts, danah boyd, and michael macy (all in US). instead, UK researchers in the area of social networks seem to be anchored to pretty "traditional research". at least, that was my impression based on the talks at UK SNA, but i will be very happy to be proven wrong ;) and there are notable exceptions in UK - eg, dunbar of oxford, bernie hogan of OII, and few others…
here are few notes taken during the talks.
cecile emery studied the relationship between big five personality traits and emerge of leaders. she considered not only leaders' personalities but also followers'. she found that leaders with high conscientiousness and extraversion tend to attract followers over time, and followers high in openness and conscientiousness tend to follow more. leader-follower pairs tend to be different on agreeableness and similar on openness.
agrita kiopa of georgia tech discussed a very interesting problem - how your friendship relations impact your work output. the main idea is that, to get something, you have to ask, so friendship becomes important also at work. they run a longitudinal national study of US academic scientists in 6 disciplines between 2006 and 2010. women are overepresented - i.e., 54% men, 46% women. friends are obtained by 6 name generators: role-based (collaborator, mentor) function-based (advice, discuss important issues), and close friends naming. 1600 egonetworks are collected as a result. so, for each person, there are 6 egonetworks. there is a considerable overlap among the 6 networks on average. full professors have more friends than assistant professors (control for tenure). the main results are that friendship has no effect in advice seeking but has effect on receive introductions and get reviews of, say, your papers. i hope she will devote a bit of future work to enemy (competitor) network. also, personality might be an interesting topic to study.
bernie hogan studied the correlates to social capital on Facebook. he used a mixed-method survey methodology and downloaded Facebook ego networks. he then focused on the question of whether your social capital is related to your (objective) network structure or to the way you (subjectively) perceive your network. Very interesting work.
tore opsahl's talk revisited the idea that small-world nets are ubiquitous. by contrast, he found that "small-world networks are far from as abundant as previously thought"
i attended netsci a couple of weeks ago. here is my stream of consciousness:
lada adamic talked about how info changes as it propagates through the blogsphere, and she effectively modelled this change  as a simple urn model. more on her upcoming ICSW paper.  her future research will go into how sentiment of memes changes/evolves (this topic has been recently covered by  jure leskovec).
former navy officer duncan watts presented few macro sociological lab experiments and field experiments that he and his colleagues run on social media sites. he showed how media sites such as Twitter, Facebook, and Mechanical Turk allow researchers to measure individual level behaviour and interactions on a massive scale in real time. the good news is that  there are already guides for running experiments on those platforms (see, for example, the tutorial at icwsm by paolacci and mason). the experiments he mentioned are fully reported in his latest book "Everything is Obvious". The main idea behind the book could be summarised as follows:
our intuition for human behaviour is so rich: we can "explain" essentially anything we observe. in turn, we feel like we ought to be able to predict, manage, direct the behaviours of others. yet often when we try to do these things (in government, policy, business, marketing), we fail. that is because, paradoxically, our intuition for human behaviour may actually impede our understanding of it. perhaps a more scientific behaviour would help us. the book is about experiments whose goal was to understand human behaviour at a large scale.
olivia woolley meza of max plank presented the results of a project that measured the impact of two events (ie, island vulcan ashes and 9/11) on flight fluxes. these fluxes were modelled as a network and metrics of interest were computed on the network - for example, they computed network fragmentation (nodes remaining in the largest connected component), and network inflation (how distances in the network decay). this study provided few intuitive take-aways, including:
- regions geographically closer to an attack are more affected
- between-region distancing is driven by hubs
with a presentation titled "A universal model for mobility and migration patterns", filippo simini (supervised by  marta gonzalez) turned to the question of whether it is possible to predict the number of flying/public transportation commuters between two locations. law of gravitation for masses (also called gravity model) doesn't work so well with people, and that's why they proposed a new migration model. the main idea of this model is that an individual looks for a better job outside his home country and, as such, accepts the job in the closest country that has benefits higher than his home country. each country has a benefit value that is a composite measure based on income, working hours, and general employment conditions. one take away was that population (& not distance) is the key predictor of mobility fluxes.
giovanni petti of imperial skilfully delivered a very interesting presentation about a project called freeflow whose partners include UK unis in london, york, kent. in this project, they have collected data from sensors placed under speed bumps that measure the number of cars that pass and the amount of time each car has spent on a bump. traffic data has been arranged in a graph and, to identify congested areas, they run a community detection algorithm on the graph. it turns out that london behaves like one large giant in terms of traffic flow because of long-range spatial correlations.
tamás vicsek studied pigeon flocks and, more generally, studied the roles according to which birds tend to fly with each other. the main finding is that each member of a bird flock takes a specific role in a hierarchy, and roles arranged also change over time
marta gonzalez studied the mobility of people living in different of cities (including non-US ones) and found few regularities: for example, she found that residents of well-off areas tend to travel nearby (maybe because their areas tend to have plenty of internal resources). another interesting point is that trip length distributions at city scale are well described by a weibull distribution. Marta also tried to reconstruct temporal activity of people living in chicago using SVD (eigen-decomposition) - temporal activity is reconstructed only using the first 21 eigenvalues (which she called eigenactivities), and such reconstruction is also predictive of people's social demographics. the main point of this modelling exercise was to show that techniques such as principal component analysis combined with k-means are great tools to detect clusters in human activity