09 Jan 09 Twitter social network leaders: navel-gazing or more?

I’m exploring the Twitter data I’ve gathered over the last few weeks, which is designed to uncover patterns of URL citations, which I believe is one of the service’s most powerful uses.  As I have written, I’m looking at Twitter as a massively parallel self-organizing point-of-view system.  In other words, my premise is that by posting URLs to Twitter, people are saying that they found a web page to be interesting and valuable.

Today, I’m looking at “centrality,” a typical social network metric.   I am interested in degree centrality, which looks at how many connections a person has, which shows who the key players are.  I’m considering two people to be connected if they cited the same URL in the same time frame, regardless of whether or not one was an explicit retweet of the other.  Later, I’ll probably weight the connections with explicit retweet and other data.  For now, I want to see if follower count, a far simpler metric than centrality, would work just as well.  Here is a log-log scatterplot of degree centrality v.  follower count.

Follower count v. degree centrality

The data points are scattered all over the place, which means that follower count does not correlate to the connections revealed by citing the same URLs.  I’m not surprised, given all the games people play to get followers, the robots and such that have little or any human thought behind them. 

As a reality check, let’s look at a similar plot that compares follower count to user mentions.  I would expect that people who have a lot of followers will be mentioned (in the form of @screen name, in a reply, retweet or any other context) more often.  Here’s the graph. 

Followers v. mentions

Bear in mind that my data gatherer is biased toward people who cite a lot of URLs, so when I say count mentions, those are mentions by people who tend to cite a lot of URLs in their posts.   As you can see, although there are many outliers, there is an obvious trend upward and to the right, which indicates a positive correlation – people with a lot of followers indeed do tend to be mentioned a lot.  The upper left area is almost empty because it is hard to get any mentions when you don’t have any followers.  On the other hand, you can have lots of followers and few mentions, which is why the there are more points toward the lower right.

Outliers are often interesting and I find myself wondering who is getting a lot of mentions even though they have very few followers.  The dot closest to the upper left corner is MsTweet, who is a “customer service evangelist for Mr.Tweet” and therefore doesn’t follow much of anyone, but gets mentioned a lot.  In the upper right border area, with lots of followers and mentions, are Shorty Awards, Chris Brogan, Guy Kawasaki, and ReTweetTrends (in the center of the top, not following nearly as many as the others).  The lower right corner outliers are people who are heavily followed, but rarely mentioned by people who cite URLs.  They include Kevin Rose, Jason Calacanis, Veronica and iJustine.  I’m surprised, actually, that these folks’ huge followings apparently either aren’t mentioning them often or aren’t often citing URLs.  Let’s reality-check that with Twitter search.

I’ll search on each of their user names, then repeat the search with their name and “http,” which will give a rough comparison of all mentions v. mentions with URLs in them.  Twitter’s search doesn’t give a result count, so it’s pretty hard to tell.  All I can go by is the frequency of recent tweets.  Let’s compare it to somebody who is mentioned a lot – Chris Brogan.  He is definitely getting a lot more frequent mentions in conjunction with URLs, so at first glance, the data seems believable.

Perhaps this indicates that the people with big followings yet few mentions have a different kind of influence.  People like Chris and Guy seem to be leading others to look outside of Twitter, while Kevin, Jason, Veronica and Justine have some other, perhaps more Twitter-centric influence.  Is it safe to say that the latter group is more engaged with Twitter for its own sake?  

It seems that some of the popular Twitterers are leading their followers mostly into Twitter navel-gazing, while others are leading people beyond what Twitter itself has to offer.  I find myself wondering how this might change as Twitter matures… and wondering if perhaps the navel-gazers are newer to Twitter and will get bored faster.  I’m gathering more of the user information now, so I should be able to compare the average number of days they have been using it.  In any event, from a business standpoint, I think I know which kind of leader I’d be more interested in.

Tags: social network analysis, twitter

05 Jan 09 Not so surprising, aggregators lead in URL scoring

I thought I’d see which Twitter users are scoring the highest in terms of posting URLs that become popular. My code gives them points based on how early they posted and how popular the URL becomes. I suppose it should not have surprised me to find that most of the high scoring users are not real people, but aggregators that feed tons of URLs.

Who is it that says that web analytics data is always messy? Whoever it is, right you are! Since a fundamental goal of the work I’m doing is to uncover interesting points of view, I need to downgrade sources that aren’t behaving as though they really have a point of view (or at least an intelligent one). I can tell instantly that I’m almost certainly looking at an automated system when I see that the “user” in question follows zero or very few people. That’s grounds for immediately downgrading. I’m not sure if I want to downgrade based on the volume of postings. Certainly beyond a believable number… and perhaps if every single post contains a URL.

Here are the top 20 sources from the last week or so, based on the criteria I described above.

1. Net2 (878)
2. techupdates (706)
3. OriginalSignal (587)
4. radi8 (565)
5. Dakshinamurti (542)
6. GaryTheGeek (453)
7. techupdate (449)
8. haripakorss (436)
9. readmashcrunch (392)
10. twittfeed (379)
11. TwitLinksRSS (359)
12. top_post (342)
13. tclauss (329)
14. TechFeed (303)
15. tc2tw (300)
16. vcsangels (295)
17. dlbrown06 (287)
18. davidsim (279)
19. mashable (272)
20. ReTweetTrends (268)
21. balduaashish (268)
22. wiredgnome (264)
23. julieti (259)
24. TechRSS (248)
25. davekresta_rss (246)

Tags: social network analysis, twitter