# Introducing The ReTweetability Metric

A topic of much recent discussion has been how do you quantify influence in social media and social networks. In the context of Twitter, much ado has been made of purely network-size-based statistics, which are easily gamed and present a shallow picture of the process of viral influence.

Extending from the work I did with the ReTweet Mapper, I’ve been exploring more intelligent metric to analyze the influence wielded by a user’s Tweets. My data has shown me that while the actual size of a user’s follower network has a positive correlation to the amount of ReTweets they get, the relationship is actually rather weak. This tells me that the actual content of a user’s Tweets may be more important to how influential that user is.

In an attempt to algorithmically study how “viral” someone’s Tweets are, I propose the following ReTweetability Metric:

This is designed to control for both the rate of Tweets the user posts and the number of followers the user has, so that this metric represents soley how ReTweetable a user’s posts are. This formula typically yields very small results so for the purposes of readability I’ve taken to multiplying it by a large constant, 1,000,000.

To demonstrate how this metric works, I’ve calculated it for the top 100 most ReTweeted users in my system which you can see below. This list is sorted by most ReTweeted, as I don’t yet have enough users’ ReTweetability metrics calculated to produce a “Most ReTweetable” list.

 User ReTweetability Metric guykawasaki 2.13909 BreakingNewsOn 2.10939 problogger 3.25695 mashable 2.45021 timoreilly 6.27744 chrisbrogan 0.382216 TechCrunch 1.71331 kevinrose 2.32571 StatSheet 1515.92 Scobleizer 0.592066 Armano 2.27349 JesseNewhart 4.07655 chrispirillo 1.85232 nytimes 0.583496 zaibatsu 0.853129 steverubel 4.18765 mayhemstudios 1.25115 codinghorror 4.69129 unmarketing 0.657318 TheOnion 7.05509 Twitter_Tips 6.81798 domestic_diva 25.6448 skydiver 5.37113 wilw 1.14165 PRsarahevans 1.15549 shortyawards 0.0536032 zappos 5.63933 twitter 14.5021 Pistachio 0.922368 davewiner 1.37601 levarburton 5.05578 Foodimentary 1.71968 tweetbomb 13.6751 themediaisdying 3.83852 perrybelcher 1.14216 AJGaza 6.19664 stephenfry 2.44443 jayrosen_nyu 5.08907 tinybuddha 98.526 lancearmstrong 1.41447 jeanlucr 4.10289 BreakingNewz 1.53622 laughingsquid 1.81378 caseywright 5.34765 mbites 4.81602 fatwallet 155.291 CNETNews 1.73988 weirdnews 3.32362 MacHeist 386.29 pleasedressme 89.7901 gapingvoid 2.33999 tferriss 16.5756 styletime 1.56265 cnn 1.10834 shelisrael 0.848424 garyvee 0.733047 BILL_ROMANOS 3.17027 darthvader 50.3311 howardlindzon 2.34331 shanselman 3.302 ColonelTribune 5.27153 redstarvip 2.33445 JasonCalacanis 0.393586 smashingmag 3.75457 ev 2.4533 barefoot_exec 0.288966 boris 15.4311 jakrose 1.02494 LeoLaporte 1.25961 manifestmmind 12.7619 mattcutts 4.91364 imjustcreative 0.891586 jowyang 0.774668 MrTweet 4.1668 sugree 1.00299 danzarrella 4.24575 OwenC 46.3504 Andrew303 10.4188 jemimakiss 3.27651 BrentSpiner 2.62709 zen_habits 13.1589 1938media 2.93775 MariSmith 0.40963 HubSpot 47.0784 cnnbrk 2.68889 copyblogger 0.807263 MarketingProfs 0.565924 Suntimes 4.33059 UstreamTV 3.67757 tamar 22.9639 BertDecker 10.9617 rww 4.83897 feliciaday 4.0208 TwitPic 26.2796 QassamCount 24.4141 gazanews 11.8672 secrettweet 0.485707 Positive_Thinkr 17.6236

If you liked this post, don't forget to subscribe to my RSS feed or my email newsletter so you never miss the science.

Warren Sukernek January 23, 2009 at 5:10 pm

Wow, great concept! An excellent way to normalize things and focus on the actual content. Can you create a table of the top Retweetability Metrics of all people with over 1000 followers or some other reasonable number?

Dave January 23, 2009 at 5:14 pm

I think you formed/explained your ratio in a confusing way. Try something like this:

(# of retweets) / (Possible Retweets)

(Possible Retweets) = (tweets in a day) * (followers)

Then, you can more clearly state that you are effectively measuring the percentage of retweets out of retweets possible. I would leave “average” out of the formula, since it’s possible (but difficult) to gather exact data (i.e., the exact number of followers at the time of each tweet), but state that in reality it is much easier and mostly accurate (except in cases of major changes in number of followers) to use averages.

Dave January 23, 2009 at 5:18 pm

It’s also disorienting that you chose to multiply the results by one million, somewhat arbitrarily. Maybe you can show the results as a percentage (something like “.015%” for Fat Wallet) or flip the ratio (1 in 6440 opportunities results in a ReTweet, again for Fat Wallet). Keeping the numbers representative of some real world value will make them much more usable and easy to understand the importance.

Stefanie January 23, 2009 at 8:26 pm

This is a great concept, eventhough I do agree that the additions dave made improve the whole calculation. Still think it is only an indicator, and the solution has to involve more variables. However, I haven’t seen a better solution so far.

Jim Lane January 26, 2009 at 11:48 pm

With the focus being on numbers, this measurement measures popularity and misses the quality factor.

Mere retweeting is one thing, but a retweet by a quality person is much different than a retweet by a spammer.

There is also the whole issue of the quality of what is being retweeted. And, then the quality of who the retweet goes to.

As a popularity metric, perhaps this works.

Quantity is always easier to understand and measure than quality. Quality calls for judgment.

jim

Bosilytics January 27, 2009 at 7:59 am

Very nice. Thank you for doing the legwork in identifing the ratio of what is starting to be the closest to a conversion in Twitter we have so far -a “RT”. Or at least one measurable by the masses.

I do agree with Dave, the presentation is much easier to comprehend, or at least makes nice since when quering results:
COUNT(like *RT*@bosilytics*) / (Opportunities)
WHERE (Opportunities) = (tweets in a day) * (followers)

Again, brilliant due to the fact that it is easily calculated, has a fair amount of normalization and after a couple trial runs I think it is something to act upon (or at least seriously consider after a little more digging).

Couple notes:
1) I think a higher value should be added to a users 2nd, and outer social cloud. While being retweeted by a follower is always great, getting RT’ed outside the cloud gets exponentially more exposure.
Problem: presently, this is hard to measure without a nice tool budget.

2) I do think the name Opportunities is more telling. THis is also more scalable as a scoring method is derived

Jim: While you will have your spammers and their network of spammers that can easily throw off this number, the shere nature of friend/folllow weeds these guys/gals out anyway. That said, until monetary value is put on a RT, why cheat?

Again, very nice. Now, who is going to make the ReTweetability calculator usign the API? …only wish it could be I.