Category: Research

I Hear Twitter

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Friendship, it seems, is more accurately demonstrated than described.  We usually don’t do a good job accurately reporting our friendships when questioned.  So, here’s a look at a slightly higher measurement of friendship: conversations.

How I See TwitterIf you squint (or click to enlarge the image) you can find a little yellow dot.  That’s me.  The connections between dots are conversations that take place within my “hearing” on twitter.  With research suggesting people as far as three degrees away from you hold a statistically significant level of influence across varied subjects; don’t you wonder who is influencing you?

Graphing Wall Street with LittleSis.org

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With a goal of transparency, wallstreetLittleSis.Org has started collecting peer-membership information for public figures of many sorts.  Just the stuff made for social graphs!

This is image represents the social networks of the CEOs of the American Wall Street companies, from the info at LittleSis.  Red nodes are the CEOs (Thain is included), and green are organizations.

The data is a work in progress, as it only represents a few organizations these folks are involved with; but a work in progress is progress indeed.

P.S. LittleSis: API pretty please!

Why is an influence metric hard to decide on?

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Why is coming to common metric for measuring influence so hard? Short answer: because measuring influence is not only nuanced, but it’s also really hard.  Maybe we’re asking the wrong question, maybe we should be asking how susceptible to influence are we?

First, a matter of semantics: authority is power bestowed by an outside source. Police, judges, your boss, etc. all have power over you in their own contexts. In most cases, authority is external to Social Media, so what we really want to know is how influential (power regardless of authority) a person is. So, I’ll stop talking about authority and start talking about influence.

Influence in SM is created through exertion of control over content that reaches you by modifying the content, or adding additional context such as your opinion. Modifications can be explicit, or implicit; merely passing along a piece of information indicates you have some interest in it. The social part of this information flow dictates who sees your content. So your influence is relative to your network. That’s bad for good metrics. What worse, from the following diagram, you can start to see that influence is also relative to the individuals within your network. Fortunately, combining influence and reach seems to be promising.

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Imagine you are A. You’re going to have a lot easier harder time exerting control over information flowing through your network than B. That’s an example, why reach (or count) alone is a poor measure of influence. But, clearly if you have a larger network, you are likely to influence more people. So how do quantity and influence work together? One strong way is path independence.

Turning influence on its head, it is much easier to measure how likely we are to be influenced depending on where the content comes from. Hearing from two independent sources will have greater impact on our decision forming than two related sources. In marketing, this is called the media multiplier when advertising is spread across multiple channels, e.g. Radio + TV.

So maybe the real question is, how influencable are we? One easy measure is network efficiency.

Random Graph Generation in Perl

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If you ever find yourself needing to generate random graphs in Perl (quite the ice breaker, I can tell you), I recommend checking out Matt Spear’s Graph::Maker, which has generators for everything from Erdos-Renyi and Watts-Strogatz to Lollipop graphs. The only downside is the use of Graph which is s-l-o-w for graphs of even moderate size, so makes using it directly for simulations of Social Networks out.

Watts Strogatz Network from Wikipedia

[Image from Wikipedia]

Happiness is Contagious in Social Networks

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Social Happines (From the NY Times)
Social Happiness (From the NY Times)

Knowing someone who is happy makes you 15.3% more likely to be happy yourself, the study found. A happy friend of a friend increases your odds of happiness by 9.8%, and even your neighbor’s sister’s friend can give you a 5.6% boost.

“Your emotional state depends not just on actions and choices that you make, but also on actions and choices of other people, many of which you don’t even know,” said Dr. Nicholas A. Christakis, a physician and medical sociologist at Harvard who co-wrote the study.

…quoth the LA Times; and there’s more coverage over at the NY Times (including a full size of that great image).

Game Theory + Network Analysis = ?; An Example

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During one Saturday in the beginning of November, I took part in a multi-party negotiation, which had some surprising results. Out of curiosity, I mapped who wanted what from whom, and ran a basic network analysis.  The second surprise of the day was the analysis was really close to the observed results. I hope this description of my network analysis of a common game theory problem spurs discussion about how use network analysis and game theory in combination.

Code Blue: Swift Trust and Team Dynamics of a Crash Cart Response

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Swift Trust, much like it sounds, is the concept of rapidly coming to intra-team trust.  A doctor friend of mine who introduced me to the term, explained it with the context of the ad hoc team of MDs and nurses responding to a cardiac arrest, a code blue.

I have been thinking about this throughout a book I am reading now, Honest Signals, by Alex (Sandy) Pentland from the Human Dynamics group of the MIT Media Lab.  In it, Prof. Pentland discusses physiological social signaling, and one point particular to swift trust stood out:  with great accuracy, one can predict behavioral outcomes using a “thin slice” of observation.  One study was able to predict six-year marital success based on just the first three minutes of a marital conflict.  There are many more studies showing similar success including job interviews, therapist competency ratings, and courtroom judges’ expectations of trial outcomes. My guess is there are things about the crash cart scenario which take advantage of this.

Some thoughts about this applied to code blue teams:

  1. the roles are well defined, so the amount of politicking is reduced
  2. time pressure pushes you to trust your colleagues, as there is little other choice
  3. the desired outcome is constrained, so you are only asked to trust in this specific situation
  4. trust develops rapidly with success
  5. trust develops when you don’t have a choice about the team over the long term. (time frame is short, so don’t know if this comes into play).

If these are right, here are a few predictions about the crash response process:

  1. there are a number of quick steps taken as a group before administering to the patient.  That would help establish some trust right at the beginning.
  2. the team members know each other at least by reputation, that goes a long way to giving the benefit of the doubt.
  3. the outcome is critical, so everyone is pushed to excel. This works in the the trust/success feedback loop.
  4. team members talk about crashes with their non-team colleagues.  this helps the reputation feedback.

Are there any MD’s or RN’s out there who care comment?  I have only the most cursory knowledge about the way the team is conducted, not to mention the actions team members take.  Does this fly?

[Photo credit: Simon]

What we can learn about social networks from contract law

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I am big fan of looking to outside fields for ideas and expertise. Case-in-point: I recently came across a reference to a great study about contract law – when people rely on the contract for enforcement during the course of business, and when they don’t. Hint: they usually don’t; they rely on the relationship.

Translating the findings to social network analysis, we come up with six great pieces of advice for all aspiring master networkers:

  1. Established relationships provide more value than new ones.

  2. Your reputation is critical to creating new relationships.

  3. The more your peer gets out of a relationship, the more you will get out of it: deliver excellence.

  4. If you are stuck together for the foreseeable future, you will both get more out of the relationship. This could be from getting forced to trust each other or pushing harder to get more out of the relationship.

  5. New relationships are easier through introductions as the introducer can punish the introducee, through reputation or otherwise, if he does not deliver.

  6. Your network is your asset and yours alone, no one is invested the way you are to maintain your relationships.

Paper discussed:

Johnson, Simon H., McMillan, John NMI1  Woodruff, Christopher M.,   (January 2002). MIT Sloan Working Paper No. 4338-02; Stanford Law  Economics Olin Working Paper No. 227. Which I found referenced in (and translated the summary from) Avinash K. Dixit’s Lawlessness and Ecomonics: Alternative Modes of Governance.