@BrandYouCindy Ratzlaff ★
The Life of a Tweet
November 4th, 2011 by Andras BenkeHow long does your content last? We took a look at the life of a tweet for influencers with different Klout Scores. We found influencers with a Klout Score above 75 have a half-life up to 70 times longer than those with a Score between 30 and 70. Messages from these high-scoring individuals stay active and meaningful for a longer time, illustrating their influence.
Tweets created by users with a Score under 30 have a longer half-life but a much lower overall volume of retweets. Those with a Score between 30 and 70 get their messages spread out to the network within the matter of minutes, but are not as adept at having their messages last longer within their network as the highest scoring Klout users. Of course, unsurprisingly, we also noticed a growth curve where online influencers with higher Klout Scores get their messages retweeted by more users.
Check out our results below or see the full image. On top is the half-life of users with different Scores. Below, we see an increase of retweets for users with higher Klout Scores.
Behind the scenes:
We used about a week’s worth of retweet data to include users tweeting on weekdays and also those who use twitter occasionally mostly during the weekends. From this data set we filtered out those retweets which originated earlier than our sample timeframe. We also cut retweets where the original tweet was created in the last 24 hours of our sample data since a significant number of their retweets could fall out of our data set. Dealing with hundreds of millions of retweet message records we used map/reduce to group these messages by the original author and calculate aggregated information about all the their re-tweeted messages.
We used about a week’s worth of retweet data to include users tweeting on weekdays and also those who use twitter occasionally mostly during the weekends. From this data set we filtered out those retweets which originated earlier than our sample timeframe. We also cut retweets where the original tweet was created in the last 24 hours of our sample data since a significant number of their retweets could fall out of our data set. Dealing with hundreds of millions of retweet message records we used map/reduce to group these messages by the original author and calculate aggregated information about all the their re-tweeted messages.
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