Utah Open Source Planet

Image by windley via Flickr

When I was building the twitterbot for @utahpolitics, I set up a test account: @uptesting that I don't use for anything. It has 38 followers even though it's just test messages and hasn't had a tweet since early January. The followes are mostly a good list of Twitter spammers or people who follow a lot of people to get a lot of followers.

Setting up a bunch of honeypots on Twitter and then adding anyone who follows them to a blakclist wouldn't be such a bad idea. Someone's probably already built it.

Tags: twitter spam security

I just set up an @uphpu on twitter. I @thinbegin have really been loving the twitter lately and think it may be a good augmentation to the mailing list, IRC Channel and WordPress tools that are already set up and being used.

If I have stepped on anyone’s toes in doing this [setting up the UPHPU twitter user], it was unintentional. I just wanted to grab it up before some other organization out there decided that the acronym fit their needs. :) Please, if there is any stubbed toes by my actions, please email me privately and we’ll mend the wound. :)

Of course, any interested twitter users among us should feel free “follow” @uphpu. I really think it would be another great “feed” of communication.

Oh, and feel free to follow me [@thinbegin] too as I post about PHP, amongst other things, regularly. Micro-blogging FTW!!! :)

Last night, I was on the floor playing with my toddler and I noticed that he was shaking a maraca — so we had a daddy/baby jam session, with me tapping on the carpet and with him shaking the maraca.  Then, I noticed what the maraca said: “AIG - Sun America”.

I thought, “hey, maybe my baby boy won’t mind if I take this maraca and raffle this thing off on shmula.com” — today is your lucky day!

Raffle Details

All you have to do is:

1. comment on this post with a valid email address or
2. retweet this post URL using the hash #aig-maraca
3. I’ll randomly pick the winner on 3/27/2009 at midnight, mountain standard time.
4. Double-entries (comment post and tweets) are allowed
5. More in FAQ below.

~~~~~~~~~~~~~~~~~~~~~~~~~~~~

Frequency Asked Questions (FAQ)

~~~~~~~~~~~~~~~~~~~~~~~~~~~~

Say it Ain’t So?

Yes, I’m afraid I’m giving this away.

Is it For Reals?

Yes, I have no idea where I obtained this one-of-a-kind collector’s item, but it is real, American International Group (AIG) maraca.

Wow, thanks for doing this!

No problem.

You are crazy, an AIG Maraca is a super rare find!

Enter the raffle, you might win!

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A few weeks ago, I posted on my experience with Twitter, Part 1.  That post was retweeted by Robert Scoble, the traffic came, got a bunch of new followers on Twitter (welcome folks), and a flurry of passionate comments on the post, including 3 comments from Guy Kawasaki.

Then, I posted on My Experience with Twitter, Part 2, followed by My Experience with Twitter, Part 3, where I offended @darthvader.

Today, is Part 4 of that series on Twitter, Brand Relevance & Brand Intelligence, and Cash Money.

Google is a great search engine, but it is, by de facto, historical.  For example, just by the virtue of the knowledge aggregation process employed by Google, as content hits the web, there is latency in indexing that content.  That very fact makes knowledge based on history.

One area that Twitter has a distinct advantage is in the collection, aggregation, and dissemination of current, real-time, information.  Twitter, as an early feedback system, is valuable to brands in many, many ways.  Here are a couple of ideas:

1. Pharmaceutical & Medical Device: In these industries, there are, by regulation, monitoring systems in-place as new drugs or medical devices are approved by the Food and Drug Administration (FDA) and enter the market.  Twitter can be a very effective and cost-effective monitoring system for Pharmaceutical and Medical Device companies.
2. Market Research: As companies develop new products, these products can be released carefully to a select number of twitterers that act as an army of word-of-mouth buzz agents.  For example, suppose I have a new product called shmula wire (a type of energy drink).  I can recruit a sample of twitterers, give each of them 10 shmula wire products with the commitment that they would give each of those to other twitterers and, hope, that they, collectively, will tweet about shmula wire.  This is literally word-of-mouth that is designed and, because it is within a buzz-system, this data is captureable, measureable, and relevant.  This is huge.
   1. Net Promoter Score (NPS) is designed to be a market monitoring, customer loyalty system.  But, the latency involved in reading verbatim comments and the increasing debate around the score itself causes companies to forget the customer in favor over methodology.
   2. Other feedback systems such surveys (inbound or outbound) have some barrier to entry involved.  Twitter is easy, self-reported, and brutally honest.
3. Communication: This is obvious, but what might be helpful for brands is the notion of pre-wiring the public of an upcoming brand to see what the customer reaction will be.  For example, prior to Coke releasing a new product called shmula wire (again, the energy drink), Coca-Cola might want to release a tweet about shmula wire to see what the reaction might be.  Based on the public reaction, Coca-Cola can make a more intelligent decision on product roll-out, messaging, and sentiment.
4. Government: The Government can monitor sentiment on a public figure, a new bill, a law, or even activities related to national security.  Twitter can be a very dangerous vehicle for terrorist; you get the picture.
5. Centers for Disease Control: Similar to the (1) above, the CDC can also monitor peanut butter salmonella outbreaks or other similar public health concerns.

The above are just some ideas for how Twitter can be more valuable to brands and also how Twitter can monetize this very valuable service.  All of the above is based on this assumption:

Brand Sentiment and Public Perception is a Function of Time; that is, the center of gravity for a brand is greatest when it is more recent.  Over time, brand sentiment loses relevancy.

Graphically, it might look something like the following:

In other words, Peanut Butter Salmonella outbreaks is most important and relevant closer to the event.  It loses importance and relevance the further we get from the event.  The real-time, feedback system that Twitter has become an authority in is ideal for anything that is contingent upon Relevance and Time.  This includes events, brands, products, etc.  This opportunity also presents the potential monetizing-yet-untapped power of Twitter.

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Twitter presents opportunities for trying things differently. With the MountainWest RubyConf coming up, I wanted to interview some of the speakers — this proved to be my chance to try things differently. I like the way it turned out. I started out interviewing David Brady (@dbrady), but Kirk Haines (@wyhaines) joined the party. Then Jim Wierich (@jimweirich) jumped in on at the end too. It

I had an experience recently where I spoke with a group of friends and acquaintances about the economy and the existential despair that is all around us.  Then, a friend said something that shocked me:

. . . it’s terrible that (company x) went through such a huge delayering

What?  “Delayering” as a euphemism for a human losing his or her livelihood — these are people who have spouses, children, a mortgage, dreams — that will be affected.  To refer to each of these human beings as a collective “Delayer” felt very wrong.

I challenged her and asked what she meant by “delayer” — through a socratic dialogue, she finally said something that was less jargon-filled:

. . . it’s terrible that so many people lost their jobs

This experience caused me to reflect on Kierkegaard’s poignant question “what does it mean to be a human?”, then on Martin Buber’s explication of the “I/Thou” relationship, then on Martin Luther King Jr.’s notion of “nobodiness” and, more recently, on Josh Bernoff’s invitation for all of us to become more human.

It’s an appropriate invitation and, in this post, wish to contribute a few ideas on how we can become more human with language: our language can convey meaning, an emotion, and a judgment.

Worldview

Martin Buber provides an appropriate context in his I/Thou description.  In plain language, we can respond to other human beings in the following ways:

1. We can treat other human beings as objects: things that either help us progress or things that get in our way of progression.
2. We can treat other human beings as humans: we can listen, have empathy, and treat each other with kindness despite other differences.

I submit that to be human means to accept Martin Buber’s description #2 above.

A few basic characteristics that will help our understanding:

1. Human development involves physical, mental, social, and the emotional.  On one aspect, Human Beings are quite complicated and have an inner-life that is rich, deep, private, and sacred to that individual.
2. Human Beings also have the faculty of memory, that can be triggered by outside stimuli, color, smell and can further trigger emotions that can sometimes drive behavior or thought.
3. Gadamer pointed out correctly that Human Beings are social creatures — sociality requires communication with others and a community is the outcome of our sociality.

All in all, it is important to remember something my mentor taught me a long time ago:

judge slowly.  people are hurting & struggling in ways that we cannot see

Having this worldview as a context is helpful — we can now discuss some ideas in how we can treat others.

Language

The above-mentioned example of “delayer” is not sensitive — clearly — but it also points to a cultural challenge in business where we have, for the most part, replaced meaningful conversations with empty jargon.  This state-of-affairs results in a community of human beings that speak, but don’t really say anything at all.

Here are some examples of jargon that I hear daily, but lack much meaning (if you are interested, Bob Sutton at Stanford, recently wrote about Jargon Monoxide):

center of excellence, ramp up, upside, collaborate, sustainable, brain storm, mind shower, metrics, multitask, green policy, run the numbers, exposure, real-time, drop the ball, goal oriented, customer oriented, level set, touchpoints, streamline, high-level, mission critical, synergy, reach out, tasked, buzz, action item, service oriented, walk the talk, reinvent the wheel, occupy the field, quick win, bottom up, core competency, circle the wagons, share-of-wallet, non-productive headcount, full-time equivalent, downsizing, redundant employee, position elimination, go to market, BHAG, delayer, organization simplification, agile, etc…

Admittedly, I am guilty of using some of the jargon I mention above.  But, my usage doesn’t justify the usage of some of those phrases — some are just cruel, whereas some are devoid of meaning, while some are actually descriptive of the concept.

I’m not saying that we shouldn’t use any business jargon, but I’m proposing a greater sensitivity toward others and a bias for meaning and humaneness — which might mean that instead of saying “drop the ball”, one might say instead “fail” or instead of saying “core competency”, one might say “what we’re good at”.

Share-of-Wallet or a Customer?

Here’s my point: would you prefer to be called “share-of-wallet” or a “customer”?  There is a clear intention in our language: “share-of-wallet” satisfies Buber’s definition of being treated like an object; being referred to as a “customer” is more humane.  I prefer to give a larger share of my wallet with businesses that treat me as a human — that is irony.

In sum, a businesses wouldn’t last long if it claimed in some form the following claim:

we are passionate about our share-of-wallet

But, a business can and should say and will likely last long-term if it claimed and behaved like:

we are passionate about our customer’s happiness

Herein lies my point and an irony: I prefer to give a larger share of my wallet with businesses that treat me as a human.

Implications of Language

Our condition as Human Beings requires us to engage in social interactions — to talk with each other; connect with each other; to commune with each other.  This means that communing with each other can be done in many types of contexts — including a business context.

Which begs the question:

Why are our interactions at home with our loved-ones more grounded and down-to-earth, but once we enter a business setting, our language and ways of communing with each other and with our customers take on an impotent tone, devoid of life or meaning?

Can you imagine if we were verbally spoken to in the same manner that newspaper print advertisements look: “50% off, buy one, get one free — shop NOW!”  We might see that in print and, certainly, on TV and other media.  But, if a human said that in-person and verbally to our face, most of us would feel that was strange.  Why?  Because it’s just not how human beings talk with each other.

Conclusion

We can all stand to be a bit more human — starting today.  Are you with me?

shop@shmula or learn about Queueing Theory

A few weeks ago, I posted on my experience with Twitter, Part 1.  That post was retweeted by Robert Scoble, the traffic came, got a bunch of new followers on Twitter (welcome folks), and a flurry of passionate comments on the post, including 3 comments from Guy Kawasaki.  Then, I posted on my experience with Twitter, Part 2.  This post is Part 3, here is Part 4, in case you were interested.  This post includes a quick analysis on the top-100 folks on twitter.

There is a camp that argues that the top-100 on Twitter, ranked by followers, is a huge homogeneous blob — that is, this is a group that largely looks the same, that primarily follows each other, and engage with one another within the top-100 in a pat-each-other-on-the-back type of relationship.

Furthermore, this camp argues that Twitter follows a long-tail model, such that “the hits” are the ones with the most followers and that the tail consists of the rest of us.  If that argument is true, then Twitter doesn’t really democratize the conversation, but that the conversation remains with the elite.

This intrigued me, so I looked at some data.  All data was as of 01/22/2009

Methodology and Data Sources

I wanted to learn about the behavior and attributes of the top-100.  So, I went to Twitterholic, pulled their data into a snappy spreadsheet, and played with the data.

Discoveries and Hypothesis

There were several things I wanted to learn more about or, even conclude, from the top-100 list.  Below are the research questions I had in mind:

1. What is the Demographics of the Twitter top-100?
2. Does tenure on Twitter likely lead to more followers?
3. For the top-100, what does the cumulative distribution and shape of updates-per-day?
4. What is the impact of Twitter updates-per-Day on followers?

Let’s address each question in succession.

Top-100 Twitter Demographics

The criticism of the top-100 as a homogeneous group really points to its demographics and, or, its psychographics.  But, the best data I could obtain provided me with very basic demographic information, but is descriptive.

The top-100 is mainly a male-dominated group, with women comprising only 12% of the group and men comprising 58% of the top-100.  For this quick study, I define “Company” as an entity — corporate or government — whose Twitter account represents that entity.  For that category, I judge that 29% of the top-100 are organizations with a Twitter account.  I define “Not Sure” as, well, not sure: this account is @darthvader — I know he is a male, but he’s also 1/2 machine and he’s also fictional.  I’m not sure how to categorize him/it, so I created the “Not Sure” category.

Other interesting information would be to study the occupation of the top-100, so see if they are homogeneous in that respect (are they all social media journalists or consultants?); other attributes would also be interesting, including geography (are they all in silicon valley?), etc.

Role of Tenure on Twitter

We wish to know whether there is a relationship between Tenure and the number of Followers on Twitter.  To do this, I obtained days on Twitter for the top-100 from registration to current day (as of 01/22/2009).  Below is a regression between the variables of tenure and followers:

Based on the best-available data I had of the top-100, it appears that Tenure almost has nothing to do with the number of followers.

Caveat: I grant that I am not an expert on the regression (though, my regression on Snoop’s “Ain’t nuthin’ but a G-Thang” is pretty darn creative); I also grant the role of outliers — for example, if I took out the outliers, the relationship of Tenure-to-Followers might actually become stronger, based on the R^2.  I considered all of that.

What does Updates-per-Day Look Like for the Top-100?

Earlier, we looked at Guy Kawasaki’s tweet behavior over time.  Do the rest of the top-100 tweet as much as he does?

For the top-100, the data above shows that the average updates-per-day is 7.7, with an average spread of 12.4 updates-per-day.  The cumulative shape of updates-per-day for the top-100 can be approximated by a normal distribution.

The ones that update the most from the top-100 are below:

1. @alohaarleen, 99.2 updates-per-day
2. @nytimes, 38.39 updates-per-day
3. @chrisbrogan, 37.59 updates-per-day
4. @perrybelcher, 34.63 updates-per-day
5. @guykawasaki, 33.65 updates-per-day
6. @newmediajim, 31.35 updates-per-day
7. @breakingnewson, 23.64 updates-per-day
8. @scobleizer, 21.73 updates-per-day
9. @mashable, 19.13 updates-per-day
10. @loic, 17.71 updates-per-day

What is the relationship between Updates-per-Day and Followers?

Similarly as before, I understand the role of outliers and how that can skey the data, but I chose not discard the outliers in this case (mainly out of laziness).  If I were actually getting paid to write this post, then I might have injected some data integrity and professionalism; instead, I’m opting for just plain fun and, hopefully, some traffic and a lively conversation.

So,

If I removed the outliers, the shape of the data shows that there might be an inverse relationship between Updates-per-Day and Followers: in other words, the more you update, the higher the likelihood that you will have fewer followers.

Again, with a more professional approach (and if some monetary reward was involved as further incentive), I’d find the inflection point at which updates-per-day begins to degrade the number of Followers.

This inflection point might be interesting to some or, might help some in their communication strategy — “when is overcommunication reached?” — for example, might be a very interesting question to answer for corporations on Twitter.

Conclusion

This was fun and I’m still very impressed with Twitter.

If you’re interested, you can download the twitter top-100 spreadsheet here.  Enjoy!

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Image via CrunchBase

I'm trying an experiment with this year's Utah legislative session, I've created a Twitter account (@utahpolitics) and set up an autofollower on it (hat tip to @jesse). I wanted to also set up a retweeting twitterbot so that people following the account would see what anyone else following the account said when it contained certain keywords.

The world probably doesn't need yet another retweeter, but I couldn't find exactly what I was looking for and decided to build one for a few reasons:

1. I like to program
2. I want to understand the Twitter API more deeply
3. I didn't want to modify someone's PHP or Python code
4. Oh, and I like to program

Armed with those few requirements and Chris Thompson's Net::Twitter library, I wrote the following program in an hour or so:

#!/usr/bin/perl -w
use strict;

use Net::Twitter;
use DateTime;
use DateTime::Format::HTTP;
use Fcntl;
use SDBM_File;
use File::Copy;

my $dt = DateTime->now;
$dt->subtract( minutes => 60); 

my $class = 'DateTime::Format::HTTP';
my $since = $class->format_datetime($dt);

my $today_string = $dt->strftime("%M");
my $seen_dir = "./seen";
my $seen_file = "$seen_dir/latest.db";
my $backup_file = "$seen_dir/$today_string.db";

# make a backup of the hash and open it
unless(-e "$backup_file.pag") {
    copy("$seen_file.pag","$backup_file.pag");
    copy("$seen_file.dir","$backup_file.dir");
}

my %seen;
tie %seen, "SDBM_File", $seen_file, O_CREAT|O_RDWR, 0644 || 
  die "Can't link to $seen_file, $!\n";

# set your own username and password here
my $user = 'put_your_twitter_screenname_here';
my $password = 'put_your_password_here';

my $twit = Net::Twitter->new(
   username=>$user, 
   password=>$password, 
   source => "Utah Politics Retweeter",
   clientname=>"UtahPolitics ReTweeter");

# find replies
my $retweets = [];
my $twit_replies = 
     $twit->friends_timeline({since => $since, count=>100}) ;

foreach my $reply (@{ $twit_replies }) {
  my $text = $reply->{'text'};
  my $id = $reply->{'id'};
  my $name = $reply->{'user'}->{'screen_name'};
  print ".";
  if($text =~ m/utahpolitics|#utpolitics/  &&
     ! $seen{$id} 
    ){
        unshift @{ $retweets}, 
	        {'name' => $name,
	         'id' => $id,
	         'text' => $text
	 } unless $name eq $user;
  }
}

print "\n";

foreach my $retweet (@{ $retweets }) {
  print ".";
  my $status = "(@".$retweet->{'name'}.") ".
               $retweet->{'text'};
  my $code = $twit->update($status);
  $seen{$retweet->{'id'}} = 1 if $code;
}

print "\n";

1;

I made heavy use of Data::Dumper to Dump data structures I got back from the library during development. This could be generalized in lots of ways. For example, passing the username and password along with the keywords to look for as arguments would allow it to be used for more than one ID. I run this as a cronjob every five minutes and so far it seems to be working fine.

Tags: perl twitter programming

Earlier this week, I posted on my experience with Twitter, Part 1.  That post was retweeted by Robert Scoble, the traffic came, got a bunch of new followers on Twitter (welcome folks), and a flurry of passionate comments on the post, including 3 comments from Guy Kawasaki.  Today, I’ll post my experience with Twitter, Part 2.

A very basic observation I’ve discovered in my 40-something days on Twitter is this: my blog post frequency has gone down and my tweet frequency has gone up.  In other words, I observe an inverse relationship between my blog post frequency and my tweet frequency such as below:

Implications

• My Twitter audience and my Blog audience are different; so, on one respect, one will benefit and the other will not.
• Conveying information and participating in a conversation is tough on Twitter — 140 characters is all one has and needing to convey more complex concepts or trying to make a point on so few characters might be difficult.  Blogging is better for that and the conversation can be had in the comment section but, since Tweeting more can lead to less Blogging, then that is a clear implication of the inverse relationship between the two.
• Cash Money: if you make revenue from your blog and nothing on Twitter, then expect to lose traffic and cash money as you tweet more and blog less.

Either/Or?

As in most things, it’s not an either/or or dichotomous situation.  One can reconcile their tweeting habits with their blogging habits.  In fact, on shmula.com, my tweets are now integrated directly on my blog which, I’ve discovered, is helping like crazy on search engine optimization (SEO) and getting indexed by Google.

Plus, my tweets can be additional or complementary content on my blog.  My tweet content provide a different type of content that is actually more human — more of my everyday life — rather informative content as is typically on my blog.  This means, then, that my readers can see a different aspect of the person behind shmula.com, not just the geek, but the human who does everyday human, non-interesting stuff.

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I’ve been on Twitter for 44 days now. In sum, I love Twitter: I find it to be a very helpful utility for both consuming information as well as for contributing to a conversation. But, I have some other observations too that I’d like to share in a series of posts. This is Part 1 of my observations on Twitter.

Tweets come in all forms — useful, useless, agnostic, and bizarre. People who tweet also fall into the same categorization: useful, useless, agnostic, bizarre, and [use your imagination]. One observation is that there are some who tweet that have a lot of followers. As I’ve scoured the people with a lot of followers and tried to make a judgment on the value of their tweets — in general — I came away lacking: why would anybody follow these people?

Case in Point: Guy Kawasaki has almost 47,000 followers. As a former follower for 2 days, I noticed that his tweets were robotic; unnatural; it felt like a bot was tweeting for him. So, I happily unfollowed him — he wasn’t interesting at all & just added noise to my already overcomplicated life.

Then, it dawned on me — that Guy Kawasaki’s tweet behavior was like a rambling drunk at a neighborhood bar: he couldn’t stop tweeting and was, for the most part, rambling stuff nobody wanted to hear.  I also became curious about the quantitative nature of Guy Kawasaki’s tweets — so, I ran some numbers.

• Guy’s earliest tweet I could find was dated November 8, 2008 — 64 days ago.
• Within 64 days, Guy Kawasaki has 16,457 tweets.
• This means the following:
  • 16457/9.14 weeks = 1800 tweets per week (tpw)
  • 16457/64 days = 257 tweets per day (tpd)
  • 16457/1536 hours = 10 tweets per hour (tph)
  • 16457/92,160 minutes = 0.17 tweets per minute (tpm)
  • 16457/5,529,600 second = 0.003 tweets per second (tps)

Looking at the raw numbers above, it’s pretty astounding that a human can tweet that much. It’s pretty overwhelming and, hence, I unfollowed him. But, why does he have almost 47,000 followers?

The Guy Kawasaki scenario led me the following hypothesis:

• Those who tweet the most useless noise have the most followers¹.

Perhaps my hypothesis can be displayed as a simple table like below:

To explain,

• the top-left quadrant says that “there aren’t very many followers for low-value tweets”
• the bottom-left quadrant says that “there are a bunch of followers for low-value tweets
• the top-right quadrant says that “there aren’t very many followers for high-value tweets”
• the bottom-right quadrant says that “there are a bunch of followers for high-value tweets”

Based on this table, I’d consider Guy Kawasaki to occupy the bottom-left quadrant.

Conclusion

I’m not picking on Guy Kawasaki at all — in fact, my comments are more of an indictment on the followers than on Guy Kawasaki, the man. He can tweet whatever he wants — but, people have a choice to follow or not to follow. For some reason or other, he still has 47,000 followers.

Let me generalize even further: beyond Guy Kawasaki — my hypothesis is a generalized theory on twitter as a community: maybe twitter is subject to the laws of Game Theory, namely, the paradox of conformity –

Conformity: People in a group often believe and do the same thing as people around them. This leads to an Information Cascade — that is, you do what other people do, etc. For example, if you are eating at a fancy restaurant and don’t know which fork to use, you naturally look to see which fork the first person used, and you use the same one. Then, the third person notices which fork you and the first person used, and he does the same. And so on.

In other words, perhaps Guy had a bunch of followers, so more joined his bus thinking that, if they didn’t follow him, they might be missing something or not be in the “in-crowd”.

So, I only use Guy Kawasaki as a case study for this post. He can obviously do whatever he wishes with his tweets. I actually like him. He is Asian — which helps and, more importantly, he’s an adoptive father — so am I (baby 1, baby 2, baby 3). So, I like him and my observations are really more on Twitter as a community of conformity than it is anything personal about Guy Kawasaki.

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Ever since I discovered Twitter, I’ve been amazed at @ev, @biz, and @jack’s idea of simplicity and usefulness.  Lately, @windley (Phil Windley’s article), @monkchips, and JP have approached Twitter from a more theoretical perspective.  This article is my contribution to that healthy conversation (this blog post will be followed by a short tweet, of course).

~~~~~~~~~~~~~~

James Governor attempts to define a pattern found in Twitter and other social media and calls it “Asymmetric Follow”.  He defines Asymmetric Follow as the following:

Asymmetric Follow is a core pattern for Web 2.0, in which a social network user can have many people following them without a need for reciprocity.  Asymmetric Follow is unlike email for example, which tends to be within small groups, with all users knowing each other (newsletters are a clear exception here). If you see a social network where someone has 5000 followers and only follows 150 back - that’s Asymmetric Follow.

So, if I were to explain James’ definition to a teenager ¹ , I might say something like:

“Asymmetric Follow is when the popular kid in school is admired by a bunch of less popular kids and when the popular kid speaks, everyone usually listens and when the less-popular kids speak, the popular kid can choose to listen or respond or do nothing”.

If I’ve been charitable in my understanding and summary of James’ definition of Asymmetric Follow, then his explanation and definition makes sense to me.  From my experience as a former High School student of the less-popular type, that’s how life was.

I have to note, however, that James’ definition confounds the behavioral economics definition of Information Asymmetry — but he means something different.  The historical definition has more to do with the direction of communication and the “stickiness” of that information and how that “stickiness” can impact decisions and rational choice.  James Governor doesn’t address the definition in behavioral economics but does use a similar term.

Twitter as Combinatorics

But, let’s quantify what James is talking about.  In fact, when we do, we’ll find out that it’s actually basic combinatorics.

Suppose there are persons A and B, who follow each other.  In this scenario there are 2 communication links (AB, BA).  Add person C who follows and is followed-by persons A and B, now we have 6 communication links, (ABC, ACB, BCA, BAC, CAB, CBA).  So, inductively, as inter-followership ² permutation grows, the raw combinatorial communication link counts grows quadratically, not linearly.

To demonstrate this, we use basic statistics of the form n-choose-r, where !, such as n!, is equivalent to n factorial, to arrive at the formula for how many pairs or permutations we can choose from n items:

For the number of pairs, we can reduce the above formula to the following:

Visually, as inter-followership grows, the communication links grows non-linearly, but quadratically (n! grows exponentially) — in either case, the function is clearly not linear:

Mutually Exclusive, Comprehensively Exhaustive (MECE)

JP runs a really fun experiment that validates his hypothesis that tweets in the universe of Twitter are comprehensively exhaustive ³.  What his experiment does not show is the exclusiveness of the tweets — that is, their uniqueness from each other.  On its face, this is not a big deal, but in scientific inquiry, being able to compartmentalize objects in unique buckets is helpful.

One reason it is difficult to classify tweets as mutually exclusive in content is because there are Replies and Retweets.  There is probably an innovative way to find the unique and mutually exclusive clusters in the corpus that is Twitter — that would be fun work for a computational linguist.

For this post, this is not a big deal, but I just make this point for clarity — really great experiment, JP.

A Conclusion

Ummm, I don’t really have a conclusion or a point, except for that I think Twitter is pretty amazing and that Twitter can and should encourage computer scientists, computational linguists, behavioral economists, combinatorial mathematicians, set theory geekzoids, game theory freakonomica, cultural anthropologists, and others to participate in and learn from this massively human experiment.

I really like Twitter — oh, by the way — retweet this post…

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