In “Using Twitter Lists To Judge Influence“, Todd Zeigler of the Bivings Report writes:
I think Twitter Lists will end up helping separate the men from the boys when it comes to influence. In addition to seeing a Twitter users follower count, we can now see the number of other Twitter users who have added them to lists (example to the right). I would argue that getting added to a list is a bigger deal than simply getting someone to follow you.
I’m certainly intrigued by Twitter Lists, but I’m skeptical that counting how many lists someone is on will prove that much more useful than follower count. For example, I currently have 1159 followers, am on 33 lists, and have a TunkRank of 24.1. For grins, here’s a handful of people who have similar stats:
- Evan Sandhaus: 796 followers, 21 lists, TunkRank = 17.2
- Josh Young: 801 followers, 25 lists, TunkRank = 14.3
- Chris Ahearn: 1108 followers, 14 lists, TunkRank = 30.1
- Brynn Evans: 1303 followers, 33 lists, TunkRank = 18.9
- Eric Andersen: 1543 followers, 37 lists, TunkRank = 3.1
While I can’t generalize from a few arbitrarily selected data points (though Gladwell seems to have no trouble doing so in Outliers), my suspicion is that list count will be highly correlated to follower count–and may actually be a noisier signal because the numbers are so much smaller.
Of course, there’s no reason we should use raw list counts–any more than we should use raw follower counts. Just as TunkRank aspires to model attention scarcity and recognizes that not all followers are created equal, an effective measure of how lists contribute to influence must recognize that not all list memberships are created equal either.
I’ve been chatting with Chris Langreiter, who is working on enhancements to TunkRank to address some of the oversimplifications of its model, as well as with Jonathan Glick and Ken Reisman at TLists. I’d like to see online influence–on Twitter and in general–measured more effectively. It will be great if lists can help, but we can’t make the same naive mistakes as those who were quick to embrace follower count as a measure of authority.
The Noisy Channel 
13 replies on “Twitter Lists as an Influence Measure?”
No, but if you put the names of those 33 lists together into a tag cloud it is rather a nice description of what you are known for….what Terrell Russell calls Contextual Authority Tagging maybe?
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Interesting. Perhaps I’m stretching the analogy between Twitter and link-based authority on the general web, but if TunkRank is like PageRank, then perhaps a list name is like anchor text.
In any case, thanks for the link–I just subscribed to Terrell’s blog!
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i think list-followers will give us an idea of authority for sure. i feel no desire to create my own hcir list, because i suspect yours is better – so i follow yours. for example. i think number of lists alone will just correlate roughly with how many people follow you.
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I agree that eventually, lists will be just as noisy. Moreover, I think lists will 1. quickly become stale and therefore not be valuable (at least follower count is current) and 2. make it even more intimidating for twitter virgins to jump on the twitter bandwagon (now you’re behind in two metrics: followers and lists).
On the measure of influence question, I recently came across Klout (www.klout.com) and liked what they are doing and how they measure influence. Have you seen it?
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I hadn’t thought of the intimidation factor, but that’s a good point. As for Klout, I have tried it, and it’s intriguing. But I find it on one hand complex (lots of factors) and on the other hand not transparent (not entirely clear what each factor means). I’d be curious if anyone is using it to benchmark a social media marketing / influence strategy.
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How much do you suppose the influence one derives from being in a particular list is different from the tunkranks of those who follow the list? Or, to put it another way, if someone Very Important were to compose a list that no one followed, would it really matter?
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Oh, also, I think lists help–or should help–the intimidation factor. It’s generally too hard to find the right folks on twitter, and ideally, lists add a ton of value in that regard.
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In principle, I see lists as a great guidance tool. But right now the process of deciding which lists to follow seems as chaotic–if not more so–than deciding which users to follow. Certainly I’d like to see a good interface for list discovery. I hope the chaos will settle out soon. Once that’s the case, I imagine that lists will be a lot like users from an influence perspective, and that list members will share in that influence.
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I am not sure that list membership count by itself will matter in the long run — I can create a network of bots (not really, but we all need dreams!) that will put each other on a bunch of lists.
In the end, you still need to do a more sophisticated authority analysis of large chunks of the social graph to understand how connected (and thus influential) someone is. Of course a more robust analysis should use more than one relation.
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I’m sure those botnets will pop up if anyone starts taking list membership counts as a strong signal–which at least reassures me that such follow will be short lived. I do hope that we see an increasing interest in more robust approaches to measuring and characterizing influence, from both researchers and practitioners.
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[…] you’re about. However, I’ve been inspired by my ex-colleague, Daniel Tunkelang’s article on measuring Twitter influence and I’ve tried something […]
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