Viral on Twitter: TweetMatrioska

This is how a curious chain of tweets encapsulated within each other was spread in space and time. I have called this experiment TweetMatrioska because as traditional Russian dolls, a tweet contained a tweet which inside contained another tweet and so on until the tweet source. (The map is made with CartoBD, the wizard used was Torque. A true wonder so powerful and easy to use) It all began on February 14 when I found this chain by @estebanmoro and @moebio. Seeking the source I found the origin, @BenHowe, not without examining nearly 40 tweets before. This experiment aroused my curiosity because it is closely related to my research on propagation and I quickly got to attempt recovery of tweets from this chain. I found this solution but I wanted to analyze this dynamic diffusion with my own resources and these are the first results of the experiment. Propagation network topology

  • Number of nodes: 10,568
  • Number of connections between nodes (multiple connections between nodes are counted as one): 11,170
  • Network diameter : 70 ( the largest chain nested tweets was 70! )
  • Average chain length : 20,54
  • Clustering coefficient : 0.006

Temporal propagation The following timeline represents the number of tweets/hour ( GMT) during the propagation.

To get the data I have followed this methodology:

  1. Find the tweet source from my chain ( 38 links)
  2. From the tweet source obtain by search API what tweets included the URL into the tweet and by a recursive manner get the chain of tweets. I found 11,339 tweets from 10,568 different users. By this method I did not acquire all tweets (I did not get elements from my chain). This could be due to two reasons:
    • The search API does not provide all the messages asked
    • If the URL of the tweet is encapsulated with an URL shortener as bitly or owly, is not found when looking for the tweet as https://twitter.com/screen_name/status/xxxxxxxxxxxxx
  3. Generate a graph with an ad hoc script for this type of propagation
  4. Find the geographic coordinates of the locations declared in the user’s profile with the Google Maps API (has a rate limit of 2,500 per day). The coordinates of the 70 % of users were obtained

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