Case study: The multiplying power of retweets
The St. Louis Zoo's Jade (Photo by Huy Richard Mach, Post-Dispatch)
Late one afternoon last week, a news release popped into my inbox from the St. Louis Zoo, announcing its 23-month-old elephant Jade had contracted a herpes virus — an often fatal condition in pachyderms. Immediately, I posted the news to Twitter on our @Weatherbird account.
Within the hour, five others had retweeted the news — four times from people within the St. Louis Post-Dispatch newsroom and once by someone else.
At the time, the Weatherbird had 181 followers, so I was just curious to get an idea about how far something can spread when it’s retweeted. By my rough estimation, those five retweets exposed the news to approximately 1,400 people on Twitter. And let me be clear: I’m estimating. A math person could calculate it perfectly. Here’s how I did it.
The five people who retweeted the original tweet from @Weatherbird were: @ericasmith, @libbylou12, @sbondioli, @garrick_s, and me. I used a Twitter app called Venn’d to determine how many followers overlapped each of the six people who tweeted the news. Venn’d lets you compare only one account to another.
I used the Google Chart API to create Venn diagrams to compare the @Weatherbird followers to the two largest retweeters. Here’s the diagram for that comparison:
I also did a Venn diagram comparing @Weatherbird with two other retweeters. I didn’t include @libbylou12 because 1) She is relatively new to Twitter and only had 17 followers at the time; and, 2) I couldn’t make Google do more than three sets!
Using the information I’d collected from Venn’d and the diagrams above for guidance, I basically munged the information. How many people did ericasmith reach? Subtract the overlaps with Weatherbird. Subtract the overlaps with me. How many did I reach, minus overlaps with ericasmith and Weatherbird? And so on. And, just so you know, I picked away at this for several days; the number of peoples’ followers changed, of course, from the day the news broke.
It’s not perfect, but I think it’s a fair estimate. One tweet to 181 people equaled exposure to another 1,400 people. Hey, it doesn’t hurt when one of them already has nearly 900 followers. But you still get the idea, especially when this story wasn’t actually retweeted that much (and I’m not even counting Twitter users who didn’t retweet — like this one).
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Kurt's Tweets- Realizing how woefully out of touch I have been with this part of the county. (@ Saint Louis Bread Company) http://4sq.com/5z5Bfh
- Just had my first Gmail phone call -- with my parents -- and the quality was fantastic. Wow. http://tcrn.ch/a8GndI
- Another day, another interview. Fingers crossed. (@ Saint Louis Bread Co) http://4sq.com/avGImL
- Helping my son learn the first 20 elements on the periodic table. Disturbed by how many more elements there are since I studied this stuff.
- Giving blood. (@ St. Martin's Episcopal Church) http://4sq.com/aiWuEk
View Comments on Case study: The multiplying power of retweets
[...] Kurt Greenbaum cleverly demonstrated the usefulness of the Venn’d Twitter app and the Google Chart API to quantify the multiplying power of Twitter. After a concise explanation of his methodology, Kurt concludes that, “One tweet to 181 people equaled exposure to another 1,400 people.” This is a great example of Twitter’s viral nature. Visit Kurt’s STL Social Media Guy blog for the full multiplying power of Twitter case study. [...]
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