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I believe it shouldn't be hard to gather and analyze such cascades with standard tools like Python. For example, "21 Receipt for Mining Twitter" which is currently a part of the second edition of "Mining the Social Web" has receipts for getting origins of retweets and users, who retweeted it:
https://github.com/ptwobrussell/Recipes-for-Mining-Twitter

I have a little different experience/research questions in this regards. Two of my mostly retweeted posts were about materials that actually anyone could see: I posted a comment on "The Majority Illusion" article from MIT Technology Review and some stuff from R-Bloggers I believe. The most weird part was that my tweet about "majority illusion" started getting second wave of retweets after weeks or maybe even a month from its publication. 

I did the literature review on SNA of Twitter data and found only a handful of relevant articles on virality, diffusion and cascades in Twitter:

So, it's still a fruitful area of research, I guess.

Best,
Alex.

вторник, 19 января 2016 г. пользователь Valdis Krebs написал:
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Folks,

If anyone is studying the viral diffusion of  messages/tweets on Twitter/onine networks, they might be interested in one of my tweets.

Normally when one tweets, if there will be a viral explosion, it happens quickly and continues quickly and dies quickly.  My tweet, started slowly and keeps going slowly — but, keeps going nonetheless.  Still being actively retweeted after 3 days. Seems to be crossing country borders in the process.  Kind of like the Turtle vs. the Hare

Here is the link to the tweet(which is from my business Twitter account), not sure if there are tools to track one tweet over time?  Some type of snowball sampling?

https://twitter.com/orgnet/status/688412071818579968

Also, for a comparison… I tweeted almost the exact same message (added “the”) from my personal Twitter account.

It was not as viral as the one from the business account… it stopped spreading after a few days. Yet, I have 3x as many followers on my personal account than my biz account.

The link to the similar tweet is here:  https://twitter.com/ValdisKrebs/status/688229707972644864

Both tweets travelled (via RT=retweet) past my network horizon(Friedkin, 1983), something that Watts found very rare in his early studies of online diffusion networks(2012).

Timing of tweets seems to be very important… who is paying attention at the time of the tweet? And then… who is paying attention to them?

Would be interested in any research that comes of this.  

Valdis


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