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.
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
The following timeline represents the number of tweets/hour ( GMT) during the propagation.
To get the data I have followed this methodology:
- Find the tweet source from my chain ( 38 links)
- 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
- Generate a graph with an ad hoc script for this type of propagation
- 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