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I hope this message finds you all well and thank you in advance for your help. Briefly, I am 
new to social network analysis, so I am seeking some guidance on edge criteria and other 
best practices. 

For a project on mood assortativity on Twitter, I have been using Bliss et al.'s (2012) paper 
on happiness assortativity in that network as a template. There, the authors define an edge 
as a reciprocal-reply and proceed to analyze the giant component produced for a week's 
work of tweets.

What makes me pause is that the ratio of replies to observed tweets currently coming 
through Twitter's streaming API is about half as large as what was reported in that paper, 
whose tweets are from 2008. Likewise, while those other authors report about 10,000 
vertices in their giant component each week, I am finding about 60 in mine (perhaps a 
reflection of a much sparser reciprocal-reply network).

So, my questions are as follows:

1) Could a change in Twitter usage account for a discrepancy of that size between my 
results and the previous paper? For example, are people simply using the reply function less 
and @mentions more?

2) Are their any publications on the best edge criteria to use on Twitter for a case like mine?

3) I think my giant component is too small to match the kind of analysis I hope to do 
(ERGM). Is it okay to analyze the entire network I have, even if the components of that 
network are disconnected?

Again, thanks for any thoughts you have,


Bliss, C. A., Kloumann, I. M., Harris, K. D., Danforth, C. M., & Dodds, P. S. (2012). Twitter 
reciprocal reply networks exhibit assortativity with respect to happiness. Journal of 
Computational Science, 3(5), 388–397.

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