22 Dec 08 Influence measurement on Twitter

Social media proponents (a/k/a people who want your money for social media products and services) urge us many ways, but it all boils down to “People are talking about you on the Internet, so you’d better pay attention.” Tapping into the swelling ground, grabbing a long tail or otherwise engaging in social media is supposed to help you make better products, faster, leading to happier customers and more money. But how do you decide who to listen to?

Success generates noise. Millions of customers means millions of comments. The first and easiest answer to this dilemma, which may be good enough – for now – is to figure out which comments are most popular. See which ones are getting the most page views, the most Diggs, Tweets or other indicators that somebody cares.

The problem with that approach is that by the time something is popular, it is often too late do so anything about it. This is particularly true in two situations:

• You are in an industry where “hits” generate the profits and are short-lived. Unless you’re growing corn, it seems like all products increasingly fit this description. Maybe corn, too.
• Your customers are angry and unhappy.

If you have a hit on your hands and you find out too late to create more inventory before the crowd has moved on, you missed an opportunity. There’s a corollary to this: the sooner you find out you have a dud, the faster you can stop wasting resources on it.

The really unfortunate thing about angry and unhappy people is that they consistently have more energy to invest in bad-mouthing than happy people have for paying compliments. That’s a well-known fact of marketing. And life. By the time grumpiness about you and your stuff becomes popular, a lot of damage has been done, obviously. Knowing who is influential can help you prevent grumpiness in the first place or do better at quelling it before it becomes popular opinion.

In mass media, the important metrics focus on popularity. Although popularity still matters, digital social networking allows us to measure influence, at least indirectly. The difference between being popular and being influential is very simple to understand in principle.

If I have 5,000 followers on Twitter, I’m obviously fairly popular. I probably am also influential. There are ways to figure that out, such as by measuring how much interaction I engage in or how many times my tweets are “re-tweeted.”

If I have one follower, does that mean I am not influential? Not if that one follower is a fellow named Barack Obama, who has more than 150,000 followers, according to Twitterholic. That is, if that Obama fellow really is following me. I mean really following me, the way we mean “follow” in the real world.

In other words, a few influential followers can be far more significant than thousands with limited influence.

I’m using Twitter as an example because I’ve been working with the Twitter APIs to see how hard it would be to come up with measures of influence. Twitter is growing on me and I think that part of the reason is the language it uses.

• Blogs and RSS have readers; Twitter has followers.
• Forums, blogs, mailing lists and such have posts or messages; Twitter has updates.
• Blogs have feeds; Twitter has APIs.

From a social network analysis standpoint, Twitter is much easier to deal with. Mostly.

Popularity is a first-order measurement. My popularity on Twitter is the number of followers I have. Influence is a second-order or greater measurement. The simplest measure of potential influence is to see how many followers my followers have. The API makes this very easy to measure (for people who aren’t so popular that the API limits become an obstacle).

My Twitter followers are followed by a bit more than 10,000 people. Pretty good, I think, since I only have 28 followers (I haven’t been on Twitter long).

My friend Dave Land has 95 followers and those people are followed by almost 175,000 others. Wow. Dave’s followers are followed by a lot more people than mine are.

Some of my followers are people I believe are influential in the world of web analytics. Let’s see how they do (a third-order measurement of my potential influence, if I did it for all of them). In no particular order:

• Anil Batra: 232 followers, who are followed by 235,000 others.
• Eric T. Peterson: 689 followers, who are followed by 373,000 others.
• June Dershowitz: 266 followers, who are followed by 122,000 others.
• Marshall Sponder: 839 followers, who are followed by 727,000 others.

Avinash Kaushik, Google’s web analytics evangelist, isn’t following me (hey, bub!), but anybody whose title is “evangelist” is supposed to be influential. At the risk of exceeding the Twitter API limits, I ran my gizmo to get his stats. Avinash has about 2,000 followers, who are followed by almost 600,000 others.

If you rank these people by popularity (followers), Avinash is No. 1, hands-down. But if you rank by potential influence, Marshall Sponder’s followers are followed by the most people, which is especially surprising given that Avinash appears to be more than twice as popular.

Dave Land comes in at No. 1 when this group is ranked by the ratio of second-order followers to followers. That means he is doing the best job of attracting followers who attract followers, which is what you need to do if you want your influence to scale beyond immediate popularity. But I should note that having a lot of followers will inevitably dilute your second-order influence, which should comfort Avinash, who came in last on that measurement (thereby saving me from last place in all three rankings).

I should note a messy bit of this measurement – sites like Woot, Twemes, hashtags.org and others that automatically follow you when you follow them. Ugh. I haven’t figured out a good way to exclude them, so I’m just doing it manually… and I haven’t thoroughly made sure I caught all of them. So there’s hope, Avinash – maybe Marshall is just signed up for more of those. In any case, don’t take these numbers too seriously. I’m going to work on some additional data points – number of replies and such, to strengthen the results.

Or can somebody save me this work and point to a site that has already done this sort of analysis? I searched but didn’t see anybody looking at second-order popularity.

Tags: Influence, twitter