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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:

   - Romero, Daniel M, Brendan Meeder, and Jon Kleinberg. 2011.
   “Differences in the Mechanics of Information Diffusion across Topics.” In
   Proceedings of the 20th International Conference on World Wide Web - WWW
   ’11, 695. New York, New York, USA: ACM Press. doi:10.1145/1963405.1963503.
   http://portal.acm.org/citation.cfm?doid=1963405.1963503.
   - Jansen, Bernard J, and Mimi Zhang. 2009. “Twitter Power : Tweets as
   Electronic Word of Mouth.” Journal of the American Society for Information
   Science and Technology 60 (11): 2169–88. doi:10.1002/asi.
   - Gonzalez-Bailon, S., J. Borge-Holthoefer, and Y. Moreno. 2013.
   “Broadcasters and Hidden Influentials in Online Protest Diffusion.”
   American Behavioral Scientist 57 (7): 943–65. doi:10.1177/0002764213479371.
   http://abs.sagepub.com/cgi/doi/10.1177/0002764213479371.
   - Wu, Shaomei, Jake M. Hofman, Winter a. Mason, and Duncan J. Watts.
   2011. “Who Says What to Whom on Twitter.” Proceedings of the 20th
   International Conference on World Wide Web - WWW ’11. New York, New York,
   USA: ACM Press, 705. doi:10.1145/1963405.1963504.
   http://portal.acm.org/citation.cfm?doid=1963405.1963504.


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

Best,
Alex.

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

> ***** To join INSNA, visit http://www.insna.org *****
>
> 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
>
> *Valdis Krebs*
> Orgnet, LLC
> Twitter: @orgnet
> http://orgnet.com
> http://thenetworkthinkers.com
> http://en.wikipedia.org/wiki/Valdis_Krebs
>
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