Susan Greenfield warns everybody (including the House of Lords) about the ‘neurological dangers’ of children using the Internet. Susan heads the Royal Institution and, from her post, influences UK social policy. Alas, she does so based on her own prejudices. Watch her and her mate Dr. Sigman (Dr. Who?) on BBC.
BBC had a lovely comment about Susan’s opinion (1:45′ in the video): “Ehm, yes, well might. But clocks were critized for making people loose touch with natural time, the printing press was accused of making people intellectually lazy, and the telephone of making them anti-social. But maybe she is right!” The BBC seems to apply The Economist’s rule “Do not be hectoring or arrogant“:
Those who disagree with you are not necessarily stupid or insane. Nobody needs to be described as silly: let your analysis show that he is. When you express opinions, do not simply make assertions. The aim is not just to tell readers what you think, but to persuade them; if you use arguments, reasoning and evidence, you may succeed. Go easy on the oughts and shoulds.
And the argument is that “we have research in social anthropology on people going from school to university, and people retiring, that shows that social networks, just like computer games, increase peoples’ social group size and activity in the real world.” (from here)
Few weeks ago, Jon (webpage, blog) gave a very interesting presentation about his new project “Social Nets“. That talk put me in a rare condition – it made me really think (quite an achievement, given my persistent vegetative state you might say) Anyhow, after the talk, I was wondering whether the Dunbar number still holds today
” Several years ago Robin Dunbar concluded that the cognitive power of the brain limits the size of the social network that an individual of any given species can develop. Extrapolating from the brain sizes and social networks of apes, Dr Dunbar suggested that the size of the human brain allows stable networks of about 148. Rounded to 150, this has become famous as “the Dunbar number”.
Is this still true? Web 2.0 tools may have well increased the size of our social circles, right? Probably not! In “Primate on Facebook“, The Economist comments on the analysis done by Cameron Marlow (the “in-house sociologist” at Facebook) and interestingly concludes that:
“What mainly goes up, therefore, is not the core network but the number of casual contacts that people track more passively. This corroborates Dr Marsden’s ideas about core networks, since even those Facebook users with the most friends communicate only with a relatively small number of them.”
The idea of reasoning about content to recommend as a similarity graph is quite widespread. Broadly speaking, you can start by drawing a set of circles (for users) on the left and a set of circles (for “items” – songs, movies..) on the right; when users rate/listen to/etc items, you draw an arrow from the corresponding left circle to the right circle (i.e. a bipartite graph). What collaborative filtering algorithms can do is project the two-sided graph to two equivalent representations, where users are linked to other users, and items are linked to other items based on how similar they are.
There are a bunch of places where this kind of abstraction has been used; for example, Oscar Celma used graphs to navigate users when discovering music in the long-tail. Paul Lamere posted graphs made with the EchoNest API on his blog. I’ve also dabbled in this area a bit, but not using music listening data; I was using (the more traditional) MovieLens and Netflix datasets. The question that comes to mind when reading about techniques that operate on the graph, though, is: are the underlying graphs real representations of similarity between content? What if the graphs are wrong? (more…)
I’ve started trying out a new service, called Mendeley. The quickest way to describe it is a “last.fm for research;” they have a desktop client that can monitor the pdf files that you are reading, and an online presence where each user has a profile. (Read about them on their blog; my profile is here). So far, it seems that they are at a very early stage. However, the basic functionality (seeing/tagging/searching papers you read) seems quite nice. On the other hand, an obvious difficulty is that of extracting accurate meta-data from research pdf files.
The similarity between research papers and songs is quite striking. Think of it this way: songs (research papers) are made by musicians (authored by researchers), have a name (title), and are collected in albums (journals/conference proceedings). Both have a time of release; both can be tagged/described/loved/hated; both are blogged and talked about. Sometimes artists make music videos, sometimes researchers make presentations or demos. (more…)
The faintly depressing human tendency to seek out and spend time with those most similar to us is known in social science as “homophily”, and it shapes our views, and our lives, in ways we’re barely aware of.
Technology, Zuckerman argues, risks making things worse: on the internet, most obviously, it’s possible to exist almost entirely within a feedback loop shaped by your own preferences
We long to have our opinions confirmed, not challenged, and thus, as the Harvard media researcher Ethan Zuckerman puts it, “Homophily causes ignorance.” (It also makes us more extreme, studies show: a group of conservatives, given the chance to discuss politics among themselves, will grow more conservative.)
The unspoken assumption here is that you know what you like – that satisfying your existing preferences, and maybe expanding them a little around the edges, is the path to fulfilment. But if happiness research has taught us anything, it’s that we’re terrible at predicting what will bring us pleasure. Might we end up happier by exposing ourselves more often to serendipity, or even, specifically, to the people and things we don’t think we’d like?
Someone is already at work: Ethan Zuckerman’s work toward a Serendipity Engine
“After much confusion, it is becoming clear what works in online video …” Hulu (Hulu Who?) seems to be successful by any measure. Online video -sharing should:
Be as simple as YouTube is cluttered
Be Web-based; no additional software to be downloaded (Joost’s biggest flaw)
(more importantly) Support advertising rather than charging for downloads. Hulu has only professional content, and advertisers love it. … Hulu now offers content from more than 110 partners. Plus, people watching tend to sit still, whereas people listening tend to move.
That’s a great possibility to submit work on trust & context management with emphasis on web 2.0 and on privacy protection. Deadline: 13th/20th of March. Submit, submit, submit This call could not be better timed.
We just finished our reading session of “Crowdsourcing User Studies With Mechanical Turk” (pdf). Very interesting paper. Few hand-written notes on which type of tasks we would run on the MechTurk.
SybilGuard’s authors will present a paper on how to defend recommender systems from the Sybil Attack.
DSybil: Optimal Sybil-Resistance for Recommendation Systems
I’m waiting to read the paper to see which real data they’ve used and how it would possibly work on typical social networks of recsys websites, which aren’t that big and may well not be fast mixing (controversial SybilGuard’s assumptions)
(doc) by Mike Thelwall: “The results showed no evidence of gender homophily but significant evidence of homophily for ethnicity, religion, age, country, marital status, attitude towards children, sexual orientation, and reason for joining MySpace. There were also some imbalances, with women and the young being disproportionately commenters and commenters tending to have more Friends than commentees.”
Reputation systems on mobile phones build “reputation scores” based on personal experiences and on other phones’ recommendations. One poorly-explored research question is when to use personal experiences and when to resort to recommendations. This paper in Biology Letter may suggest few future research directions:
Anyhow, after the talk, I was wondering whether the Dunbar number still holds today
That’s a great possibility to submit work on trust & context management with emphasis on web 2.0 and on privacy protection. Deadline: 13th/20th of March. Submit, submit, submit 
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