Yesterday, Stefan Swanepoel published a list of 100 influential and interesting people within real estate. It’s an interesting list and got a few of us (myself included) a bunch more followers.
However, set aside for a minute that he missed a whole bunch of influential people (which he is already revising) the reality is that a lot of people on his list just aren’t that interesting (and many border on being twitter spammers). If you’re a real estate professional new to twitter and you started following some of those people, I can only imagine twitter would start looking like a big wasteland of crappy tweets.
However, I think a list of influential people could be a really good thing, especially for people new to twitter… I’ve had this idea for measuring “twitter influence” within a community, and Stefan’s project finally pushed me to build a prototype. The idea is to measure, as objectively as possible, the influential people within a twitter community.
My theory and calculations are described below, but first, here’s the list:
Name |
|
Peer Rating |
| Andy Kaufman | AndyKaufman | 100% |
| Dustin Luther | tyr | 100% |
| Rudy Bachraty | trulia | 100% |
| Jeff Turner | respres | 100% |
| Teresa Boardman | TBoard | 100% |
| Kelley Koehler | housechick | 100% |
| Jay Thompson | PhxREguy | 100% |
| Daniel Rothamel | RealEstateZebra | 100% |
| Ginger Wilcox | gingerw | 100% |
| Robert Hahn | robhahn | 100% |
| Brad Nix | bnix | 98% |
| Jeff Corbett | JeffX | 98% |
| Heather Elias | hthrflynn | 98% |
| Nicole Nicolay | nik_nik | 98% |
| Mike Simonsen | mikesimonsen | 98% |
| Jeff Bernheisel | JBern | 98% |
| Joseph Ferrara | jfsellsius | 95% |
| Jonathan Washburn | JonWashburn | 95% |
| Pat Kitano | pkitano | 95% |
| Drew Meyers | drewmeyers | 95% |
| Marc Davison | 1000wattmarc | 95% |
| Jim Cronin | RETomato | 95% |
| Matt Fagioli | MattFagioli | 95% |
| Brad Coy | BradCoy | 95% |
| Mike Price | mlbroadcast | 95% |
| Nick Bostic | nbostic | 95% |
| Dan Green | mortgagereports | 95% |
| Kim Wood | KimWood | 95% |
| Todd Carpenter | tcar | 95% |
| Mike Mueller | MikeMueller | 95% |
| Sherry Chris | BHGRE_Sherry | 95% |
| Derek Overbey | doverbey | 95% |
| Ricardo Bueno | Ribeezie | 95% |
| Loren Nason | lorennason | 93% |
| Ines Hegedus-Garcia | Ines | 93% |
| Jim Duncan | JimDuncan | 93% |
| Jason Sandquist | JasonSandquist | 93% |
| Dale Chumbley | DaleChumbley | 93% |
| Missy Caulk | missycaulk | 93% |
| Kris Berg | KrisBerg | 93% |
| Brad Andersohn | BradAndersohn | 93% |
| Maureen Francis | MaureenFrancis | 93% |
| Lani Rosales | LaniAR | 93% |
| Stacey Harmon | staceyharmon | 93% |
| Bill Lublin | billlublin | 93% |
| Eric Stegemann | EricStegemann | 93% |
| Judy M | realestatechick | 93% |
| Joel McDonald | joelrunner | 93% |
| Reggie Nicolay | Cyberhomes | 93% |
| Morgan Brown | morganb | 91% |
| Mariana Wagner | mizzle | 91% |
| Paul Chaney | pchaney | 91% |
| Jim Marks | jimmarks | 91% |
| FrancesFlynn Thorsen | FrancesFlynnTho | 91% |
| Benn Rosales | BennRosales | 91% |
| Nick Bastian | RailLife | 91% |
For those interested, here’s how I calculated the influential people within the real estate community.
Step 1: Starting with Stefan’s list, I took 10 people in real estate who were following between 100 and 1000 people AND had more than 1000 people following them. My logic here is that I was looking for active twitter users (i.e. it’s hard to get over 1000 followers without being active) who pay attention to who they follow (i.e. they don’t “autofollow” or “mass” follow people). I was explicitly *not* looking to start with a list of the most influential people, but rather use some thoughtful people within the community to jump start the process. As you’ll hopefully see, the people don’t really matter much in terms of the final results, but here they are anyway: jburslem, RETomato, 1000wattmarc, robhahn, spencerrascoff, hthrflynn, JeffX, nbostic, PoppyD, ardelld. (note: Stefan’s list didn’t include enough people that matched my criteria, so I ended up grabbing a few people out of my twitter stream who did).
Step 2: Using the Twitter API, I created a list of ALL the people these 10 people are following. At this point, everyone is just a number and I won’t see anyone’s twitter name until the very last step.
Step 3: I put all of these twitter IDs in a big list and used a pivot table to give me a count by ID #.
At this point, I have a pretty good list of people within the real estate space. I think it’s pretty safe to say that if someone was “influential” (on Twitter) in real estate, then they’d be on the list of 4000+ people this process created… and most likely near the top since they’re likely being followed by this group if they’re influential. However, it’s time to expand the scope way beyond these 10 people.
Step 4: Now I took EVERYONE who was being followed by at least 8 of those 10 people (45 total) and looked at ALL the people they followed. Because some of these people were following thousands (sometimes tens of thousands!), this turned out to be a huge amount of data… although it all fit nicely in an excel spreadsheet, so I kept going.
Step 5: Starting with a base of people who were being followed in step 3 (4000+), I did a count to see how many times those people were being followed in the HUGE lists that were created in Step 4. (The idea here is that if someone was “influential” they would have at least shown up in the 4000+ IDs that were generated in Step 3 and now I was just counting how many times they showed up within this list of 45 people)
Step 6: I then sorted this list and based on the number of followers that any given ID had, I gave it a “peer” ranking that is simply the total number of followers divided by 44. A peer ranking of 100% means that out of the people created in Step 4, 44 were following that person. A ranking of 91% meant that 40 were following that person.
Step 7: I sorted the list, used Twitter’s API to reverse lookup people’s usernames (and real names), and copy-and-pasted the results above.
It’s also worth noting that I *could* take this list further and displayed the “top 100” or “top 200”, in which case we would have caught some great names that just didn’t make the cut (David Gibbons, Joel Burslem, Hilary March, Ben Martin, Susie Blackmon, Kevin Tomlinson, and Stefen Swanepoel come to mind), but I had to stop somewhere, so I decided to stop at 50 (although since 7 people tied for 50th, there’s actually 56 people on the list!). Nonetheless, if there’s interest, it’d be pretty easy to expand the list…
Final thoughts
What I really like about this approach is that it’s completely determined by our real estate peers. Like it or not, there’s no better indication of your twitter influence than the “vote” your peers give you when they follow you… and while a “total” follower count is meaningless in terms of influence within a group, if you look at the “influentials” in a relatively objective way (as I’ve done here) and track who they are following, the result is a very non-spammy, highly influential group of people within the real estate twitter community.
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