How Would You Promote Education through Social Networks?

I was recently asked to put together some thoughts about the potential impact Social Networks could have on education by a really savvy M.D. over at Cerner, and I thought this audience might be interested too.  I have not seen too much about this topic, and would really like to hear your thoughts.

Peer Learning & Diabetes
Peer Learning & Diabetes

Social Networking sites simplify conversations by lowering the cost to communicate to large groups, both for the speaker and the recipient.  Accomplished by enforcing small messages, recipients can easily consume or ignore the content with trivial effort.  This in itself has some pretty interesting impact on one’s social network.  But, the messages are also semi-permanent,  consumed at leisure, and are often open to any interested peers, not just the intended recipient.

Open recorded dialog offers a unique value in communication:  the conversation doesn’t end just because it has stopped.  This persistence and openness in the dialog has some interesting conceivable implications for education:

  1. Participants can join into the conversation, well after it’s stopped. This is a biggie.  This open availability allows individuals to pick up the conversation where it left off, taking it in new directions their own context brings with them.  It is this factor that not only contributes to many new ideas, but also helps drive quality by squeezing the most out of existing ones..
  2. Discuss once, available to all.  The SN creates a naturally accumulating body of knowledge, available to all with thanks to your favorite search engine.
  3. Record the process, not just the answer. Following along with a conversation, you can actually learn with the participants, not just gain from their answers.  Further, many times the conversation is not going to answer your specific question, but you can gain insight from the ideas already discussed, and get pointers to more places to look.
  4. Don’t have to know conversation partners in advance.  In conjunction with (1), you can put a question out to your peers, and see who responds.  Find experts, even when you don’t know where to look.

Much of the above is available to any generic SN, from MySpace to any online forum.  But, what SNs offer over and above online forums, is trust.  The who you are carrying on conversations with, you know.  You know whether the respondent is knowledgeable or guessing, and can more likely read into the subtlety of their responses. Couple this trust with focused goals, as SERMO has for the medical community, and you open the pool even wider for advice.

You’ve noticed that the language I am using is around conversation, dialog, and advice.  Because of the short-form messaging, SNs are much more suited to peer-based education than seminars.  I have yet to see anyone artfully present more than maybe 1,000 words on the Internet; it’s no substitute for medical journals.  It is, however, an excellent place to discuss the journal contents, grind out all the last subtleties, and come up with ideas for your next article.

It is the pressure of our peers, after all, that gives us the support to try things we otherwise wouldn’t have.  — BILL TREASURER, Right Risk

In addition to these benefits, there are the possible benefits of all of this being a social venture: cultural norms.  If you, the educator, control a network, there’s a lot you can do to build group behaviors to reinforce whatever you are trying to teach them: group rewards if 90% of the class does their homework, peer pressure to go outside and exercise for asthmatics, peer support in the middle of the night not to give in to that nicotine craving, or even just introducing icebreakers prior to a convention.

Each of these has been around long well before the prevalence of SNs.  But today, SNs now provide an easy platform that automates much of the hard work, and create a reason for pools of trusted colleagues to come together spanning many timezones.  From your colleagues, and from their colleagues, ideas and new perspectives arise.  It requires motivation on your part, but this is prime time for peer education.

So, how have you used SNs for education?  Constructively used peer pressure in an educational setting?  What’s your example of peer pressure helping you?

[Photo credit: Chris Corrigan]

Predict Attention in Social Networks

People distribute attention according to a power-law distribution.

Power-laws have long been associated with distribution of quantity of links individuals in social networks have. My on-going research suggests that power-laws not only describe distributions at the network level, they also describe distribution at the individual level. We communicate in a power-law distribution with our contacts, by frequency. Initial analysis also suggests we spend time communicating with each other according to a power-law.

The distribution analysis for frequency was conducted across six social networks of various types ranging in size from fewer than 100, to more than 6,000 individuals. Most SN research has been conducted on smaller networks (fewer than 100 individuals); so testing across a wide range of sizes both confirms earlier results and suggests that size is not a factor in the power-law distribution. I was concerned about possible distortion on small networks due to implications from Dunbar’s Number. It turns out that small networks are indeed different, which I am not going to go into here, but they still fit these distributions.

Analysis on any complete sub-set, will still fit these pattern. By complete, I mean that connections between any two individuals in the sub-set, must be the same as in the whole set. The value is the introduction of the ability to sample, and to operate over a network recursively. Similarly, much information can be gained about a larger network, even if the data you have is incomplete.

This distribution may allow us to accurately predict impact of changes to any social network. By measuring the current state, we can estimate the impact of adding/removing people and connections. This could be of tremendous value pursuing in any social goal creating by facilitating cohesion, culture, and the like.

I intend on publishing the results and methodology. If you are looking for that level of detail you’ll have to wait, but mail me (erich at if you would like to discuss.

Visualize Your Network with Fidg’t

figd't screenshot
figd’t screenshot

There are more and more great tools getting developed for visualizing our social networks. One of the more beautiful ones I have come across is Fidg’t.  While not quite a SN visualization tool, it does operate on data from SN’s.

Fidg’t is an interactive display that looks at your tags in Flickr and LastFM, and shows the relationships visually.  There’s even a movie of the tool in action.

Available for Mac, PC, and Linux.

Social Networks and Sales

This Guy Sells the Big Money!
This Guy Sells the Big Money!

From eponymous Social Network data alone, I can tell you who has, for any group, the most influence, who the leaders are, and who you need to convince in order to turn the opinion of the group as a whole. The question is are you going to be a trusted adviser, or a hard sell?

This ability to analyze a network often causes a knee-jerk reaction of unease by people new to the field, myself included when I first started. But, after considered thought and discussion, there are no new ethical questions here, just the same old difficult ones. First, a discussion of sales.

We all have friends whose opinions we trust above others about certain product classes. My brother-in-law is an incredible and studied amateur photographer (not that I can ever get him to update his gallery), and to him I turn for all things photographic. Another friend is an insatiable and articulate consumer of modern fiction, and whom feeds me many great book recommendations. For electronic gadgets, I turn to yet another. I trust their judgment and opinions; if you can convince them that your product is great, you have gone a long way toward convincing me. Further, switching to the general, we look to our gurus for information and ideas about the new. If you as a manufacturer/service/producer bring new ideas to my gurus, you are helping them seek out new information, which they tend to do naturally.

So, as a Social Network provider, or as a consumer of social network data for sales and advertising, you have a choice: treat networks as just another advertising platform, and be treated by the participants as just another advertiser; or provide value into the network, and reap the rewards.

[Photo by bonkedproducer]

Friendship: #1 factor in whom we spend time with

Mobiles & Communication
Mobiles & Communication

Like all good science, analyzing social networks sometimes works out to proving things we always thought were true. Sometimes, we never even had any idea how right we were. For example, we really do spend more time with people we like.

A few really bright folks from MIT and the Kennedy School, have a paper pending publishing:

[analyzing] 330,000 hours of continuous behavioral data logged by the mobile phones of 94 subjects, and compar[ing] these observations with self reported relational data.

Three significant conclusions:

  1. Self-reported data shows a mildly positive relationship with observed data, but is exceptionally noisy.
  2. Friendship outside of work is the best indicator of who spends time with whom at work.
  3. Physical proximity is a good indicator, and predictor, of friendship (and not-friendship).

So, what do these conclusions suggest for practitioners?

Observed vs Reported Data: Surveys are great for all the reasons surrounding explicit participation, but the bias effects are significant. Find a way to marry active participation with empirical exploration and analysis of social networks.

Friendship and After-hours: Don’t under estimate the power of emotion on business decisions. Since we’re more likely to agree with data that confirms any already held thoughts, let’s be realistic and recognize the impact that, viewed through friendship, has on communication in our firms.

Proximity and Friendship: While I was unable to tease out any correlation/causation relationship from this paper, if we consider friendship as a proxy both for trust and ease of ability to work with (through shared history, goals, culture, etc.), there are some solid implications on the upper limit to the value of outsourcing.

[Photo by Ed Yourdon]

Great Work, Lousy Title

13th Century Social Network of Deeds in France

Good news, from Roland Piquepaille over at ZDNet…

According to Nature News, a team of French researchers has used medieval documents to create the oldest detailed social network ever constructed. The mathematicians and computer scientists looked through thousands of records of land transactions dating back as far as 1260 in a Southwest part of France.

Makes me wonder why I did not come across it earlier. Oh, right, because they titled the paper Batch kernel SOM and related Laplacian methods for social network analysis. Shame on you French Scientists, don’t hide the good stuff.

Lower Limits on Social Network Analysis: Just Ask

Are there lower limits to the size of Social Networks worth analyzing?  The upper limit seems to be a function of how much time (and compute power) you have, but the lower?

When we do analyses, these are certain characteristics we look for as verification that our models hold.  A key characteristic is the shape of the distribution of the number of relationships.

Senate Observed

Power-law relationships in social networks are so pervasive, we are surprised when we do not see them.  The co-sponsorship of Senate Resolutions, is one such example where we do not see a power-law distribution.  In the current session, the 110th, the distribution is just about as different as you can get from a power-law: the result is linear (R-sq > 0.97).  My hypothesis is the number of Senators is just too few for the power-law distribution to emerge.

Since there are some significant constitutional barriers to increasing the number of Senators and taking a new sample, in order to test this hypothesis, I opted for locating a different larger set.  Wanting to find a network with similar culture and behaviors to control where possible for other variables, I analyzed the House Resolutions from the same period.

House Observed
House Observed

Running the same test across the House Resolutions (1,986 resolutions vs 784 in the Senate), the shape was still not best fit to a power-law.  Instead, a logarithmic fit was near perfect (>0.99 R-sq).  So, while his does not prove my hypothesis, the shape of a log fit is similar to, but less dramatic than, power-law. This result certainly suggests further exploration.

One possible logical conclusion is distribution forms change with scale.  Much of the research on social networks  has been on large scale networks, where the math is at its most difficult.  At this smaller end of spectrum, especially with the Senate, the networks may be small enough that other analyses could be simpler.

Anthropologist Robin Dunbar, has done research showing humans can keep the interrelations of about 150 people in their heads.  More than that number, and we are out of luck.  With this in mind, it would make sense that relationship/contact distribution would stretch and distort as the number increases in scale.

So, if you are looking for the key members of a network smaller than “Dunbar’s Number,” there is an easier way to find out who they are: ask.  If you can get a few people who are already invested in the network to answer you truthfully, they will be able to give you a really good idea who the key people are.