If you’re a Twitter user like me, you’re probably familiar with the way that conversations can easily by tracked by following the #hashtag that participants include in the tweets to label the topic. But what causes some topics to take off, and others to die on the vine? Does the use of retweets (copying another users tweet to your own followers) have an impact?
If you’re a Twitter user like me, you’re probably familiar with the way that conversations can easily by tracked by following the #hashtag that participants include in the tweets to label the topic. But what causes some topics to take off, and others to die on the vine? Does the use of retweets (copying another users tweet to your own followers) have an impact?
To look at this question, R user Tony Hirst dissected the anatomy of one Twitter conversation under the hashtag #ukgc12 using the twitteR package and his own R script. The visualization he created (using the ggplot2 package) is a a kind of anatomical dissection of a Twitter conversation:
Read the chart from the bottom up: each dot is a tweet. Each new participant in the conversation is listed on the vertical axis – those Twitter users with several dots in their row were active participants. You can see the conversation is quite “bursty”: active periods of replies and retweets punctuated by short pauses. The red dots are “traditional retweets” (beginning with the characters “RT”) — as you can see, the new bursts of conversation are generally preceded by a new tweet (T), not a retweet (RT). And as you’d expect with any conversation, the rate of chatter (the slope of the line) reduces over time.
As far as I can tell, this analysis doesn’t include new-style retweets (which rebroadcast to followers but do not create a new, distinct tweet), but it would be interesting to see how they fit into this picture as well.
OUseful.Info: Visualising Activity Around a Twitter Hashtag or Search Term Using R