When it comes to social media for business, there is one question on everyone’s mind: Who are the influential people in my area? Unfortunately answering this is easier said than done. Take Twitter for example. You could look at a user’s total followers or the number of lists they are on, but those are blunt instruments at best. When you’re focused on a specific topic, those numbers can be downright misleading.
After mulling this over, I figured a good measure of potential influence would be how well networked a person is in a particular topical environment. To test this hypothesis I decided to look at an area I know pretty well: the Washington DC tech scene. Since I already have a good sense of this community, I could verify the analytical results from my own knowledge.
After doing my analysis, here is my ranking of the top ten most networked individuals:
Overall, this squares pretty well with my knowledge of this community. Number one, @corbett3000, belongs to none other than Peter Corbett, the CEO of iStrategyLabs, a leading DC technology firm and organizer of many DC tech conferences (including the upcoming 10-day DC tech festival). Coming in second is Shana Glickfield, who goes by @dcconcierge, and is DC’s consummate networker and FOMO sufferer. Rounding out the top three is Frank Gruber, @frankgruber, CEO of TechCocktail.com. Number four is every one’s favorite Microsoft staffer, Mark Drapeau who tweets at @cheeky_geeky.
It is particularly interesting to see how a large Twitter following does not necessary translate into a significant number of relationships within this particular network.
For kicks, I threw the top forty accounts into NodeXL to see what the network looks like:
And the big question: how did I arrive at these results? Here is the process I used:
- My starting point was trying to figure out how to measure the number of connections within a particular geographic region and subject area. For this, I needed a good index of who was active in the DC tech sphere on Twitter. Fortunately, this part of the job has been done by the community in the form of Twitter lists. I trolled through a large selection of Twitter lists looking for ones that had “DC” and either “social media” or “technology” and entered those info my database.
- I then went through each list and saved all the individual accounts on that list.
- From the users on these lists, I ended up with a database of about 2700 Twitter accounts that, through the Twitter lists, were related to the DC tech scene.
- In the most time-consuming part of the analysis, I set-up a system to download all the people these Twitter accounts followed. Since a follow is an expression of interest in the followed account, I counted that as a “vote”. This is much the same way Google considers a link a vote of confidence in the linked page.
- After a few days of downloading data from Twitter, I had a database of nearly 2 million follower-to-followed relationships. Using this index, I checked which accounts were most frequently followed and ranked them according.
- I was able to automate nearly every set after selecting the Twitter lists, but the final step requires a good deal of “eyes on screen”. The final list included a number of very widely followed accounts, but not ones I was interested in. Since I was only focusing on individuals in DC, I removed a lot of institutions and people outside of the city. For example, the top three @mashable, @barackobama, and @techcrunch are all widely followed, but not members of the DC tech scene. As such, I excluded these from the final results. (It is interesting that Mashable has a much wider following in DC than Techcrunch. I imagine if I was looking at Silicon Valley the ranking would be inverted.)
I was able to automate most of the above steps using a small app I coded, so much of the data collection took place while I was fast asleep in bed. Due to Twitter API data call limits, most of this was done using automated CRON jobs. Steps 1 and 6 though were not automated and required diligently going through the data.
So that’s it? Certainly not, this only measures who is widely followed within this topical subset, not who is actually influential. You’d need to combine this with other measures (frequency of retweets, ability to drive conversations, and so on) to get real sense of influence.
Fortunately the results do square pretty well with my understanding of the DC tech scene, which helps validate the approach. Most likely I’ll do some more playing around with this technique, so stay tuned.