We looked at Twitter Retweet API recently and have been collecting retweets time series for a few weeks. One interesting exercise is to back up the paths by which the tweet goes from its original writer to all the subsequent retweeters, and we’ve been able to produce a few graphs.
- Perhaps this is the most common pattern in our sample. Most of the retweets come from the immediate followers of the root (who writes the tweet) and there are a few more done by the followers of 1st layer retweeters.
- Here the root’s followers are still the group of people who contribute most, and that’s why the root is clearly identifiable at first sight. But we do see a lot of longer paths.
- This one is more interesting, since the “landscape” looks so “even” as opposed to the previous two. The root is no longer instantly identifiable. (Actually it’s at middle left.) Also the average path length from root to leaf is longer.
- The fourth might be another extreme. The “landscape” is still uneven, but in this case the peak is not root. The root(at lower left)’s immediate followers only contribute 7 retweets. The tweet travels 5 steps to arrive at a user whose followers’ retweeting champions all other sources.
Generally, during the period of retweeting, the root’s number of followers increases. Here is a graph showing the relation b/w number of retweets and the increase of followers.
Of course these are a few facts, but not conclusions by any sense. Economic analysis is on the way.
Update: you can find a pdf file of nearly 900 retweet graphs on the Project page.
Great work, Michael.
On the last analysis, I think it would be more interesting to take a zoom-in view and remove some outliers. Now the trend is not that strong. It looks like the correlation weakens as the # of retweeters increases.
Btw – how did you get the “network graphs”? Mind sharing the R code?
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