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 |
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BreakingNewsOn | 2.10939 |
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problogger | 3.25695 |
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mashable | 2.45021 |
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timoreilly | 6.27744 |
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chrisbrogan | 0.382216 |
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TechCrunch | 1.71331 |
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kevinrose | 2.32571 |
 |
StatSheet | 1515.92 |
 |
Scobleizer | 0.592066 |
 |
Armano | 2.27349 |
 |
JesseNewhart | 4.07655 |
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chrispirillo | 1.85232 |
 |
nytimes | 0.583496 |
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zaibatsu | 0.853129 |
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steverubel | 4.18765 |
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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 |
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PRsarahevans | 1.15549 |
 |
shortyawards | 0.0536032 |
 |
zappos | 5.63933 |
 |
14.5021 | |
 |
Pistachio | 0.922368 |
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davewiner | 1.37601 |
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levarburton | 5.05578 |
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Foodimentary | 1.71968 |
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tweetbomb | 13.6751 |
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themediaisdying | 3.83852 |
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perrybelcher | 1.14216 |
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AJGaza | 6.19664 |
 |
stephenfry | 2.44443 |
 |
jayrosen_nyu | 5.08907 |
 |
tinybuddha | 98.526 |
 |
lancearmstrong | 1.41447 |
 |
jeanlucr | 4.10289 |
 |
BreakingNewz | 1.53622 |
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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 |
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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 |
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smashingmag | 3.75457 |
 |
ev | 2.4533 |
 |
barefoot_exec | 0.288966 |
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boris | 15.4311 |
 |
jakrose | 1.02494 |
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LeoLaporte | 1.25961 |
 |
manifestmmind | 12.7619 |
 |
mattcutts | 4.91364 |
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imjustcreative | 0.891586 |
 |
jowyang | 0.774668 |
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MrTweet | 4.1668 |
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sugree | 1.00299 |
 |
danzarrella | 4.24575 |
 |
OwenC | 46.3504 |
 |
Andrew303 | 10.4188 |
 |
jemimakiss | 3.27651 |
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BrentSpiner | 2.62709 |
 |
zen_habits | 13.1589 |
 |
1938media | 2.93775 |
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MariSmith | 0.40963 |
 |
HubSpot | 47.0784 |
 |
cnnbrk | 2.68889 |
 |
copyblogger | 0.807263 |
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MarketingProfs | 0.565924 |
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Suntimes | 4.33059 |
 |
UstreamTV | 3.67757 |
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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 |