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Thanks Jim. Of course, I've read your papers (and in particular I'm
familiar with the work of yours and Mucha's with coworkers).
Blockmodeling is certainly an option for the network analyses we are
going to do in the future and I would be honored if you and Peter
might want to collaborate with us on these data. Another option is
community detection that I think all of us are interested (just to
mention Mucha's work with Mark Newman, Mason Porter etc.).

However, I need to explain something on the structure of our networks
that I've left it vague in my previous email. Actually, I need to
describe how these networks were generated. The answer is by
proceeding through the following 2 steps:

(1) Collect a sample or possibly "all" tweets containing a trending
topic hashtag in their contents. By "all" possible tweets, I mean
running a script that harvests all tweets with the requested keywords
from the time it starts until it stops.

(2) Among all these tweets, we are filtering only those which are
retweeted (RT) or mentioned (@).

Consequently, each one of our networks corresponds to a particular
searched keyword (hashtag) at a particular time period and it is
represented by a directed graph, in which nodes are tweaples (Twitter
users) and directed edges (arcs) from node u to node v correspond to
tweets containing the searched keyword which were either originally
sent by v and then retweeted by u or u was answering (mentioning)
something to v always including the terms of the search.

I hope it is more clear now what sort of networks we are dealing with.
Note that such networks are typically disconnected but in some of them
one can isolated rather large components with not necessarily a
star-tree structure, something which signifies how vivid are the
communicative exchanges through Twitter (in the context of these
protest events).



On Wed, Jun 12, 2013 at 3:00 PM, James Moody <[log in to unmask]> wrote:
> Hi Moses -
> I think to be most effective, this sort of many-nodes-over-many-times bit
> can really benefit from some fairly simple aggregation.  In this case, you
> might want to block-model to identify positions ("always followers"? or
> "initial senders"? or some other?) collective representation that can help
> distinguish the many points to substantively interesting positions.
> This is the strategy Peter Mucha and I used for visualizing changes in the
> US Sentate co-voting network here:
> Moody, James and Peter J Mucha. 2013. "Portrait of Political Polarization"
> Network Science 1:119-121.
> PTs
> Jim
> -----Original Message-----
> From: Social Networks Discussion Forum [mailto:[log in to unmask]] On
> Behalf Of Moses Boudourides
> Sent: Wednesday, June 12, 2013 5:02 AM
> To: [log in to unmask]
> Subject: Visualizations of ongoing protest Twitter networks in Turkey
> *****  To join INSNA, visit  *****
> Hello,
> I would like to inform you about a Gallery of protest Twitter network
> snapshots until today that you may see at:
> n%20protests%20in%20Turkey%20summer%202013?sort=2&page=1
> Here is the slide show:
> 0on%20protests%20in%20Turkey%20summer%202013?sort=2
> These are snapshots of Twitter networks created by RTweeting Tweets under
> certain trending hashtags sampled at various time slots of short duration
> after June 2, 2013, almost daily. The content of these tweets refers to the
> ingoing protest events in Turkey in summer 2013.
> Actually, we are working on a project, in which we intend to do various
> network analyses and to give proper political and cultural interpretations
> of the protest networks observed through the patterns of Twitter
> communication compiled in our datasets. In addition to these network
> snapshots, we are collecting streaming Twitter data on these events, which
> are really Big Data in size. When written, a first report and the final
> manuscript documenting the results of our project will be posted here.
> However, those of you who are interested in assisting in improving these
> network visualizations (or otherwise give us constructive suggestions about
> the forthcoming network analyses of such Big Data) are welcomed to
> collaborate with us. Please, contact me if you're interested in this.
> Valdis? Lothar? Vlado? Skye? (Just mentioning a few colleagues who are in
> the network visualization area and apologies if I might have forgotten
> anybody else in the socnet community.)
> All the best,
> --Moses
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