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On Twitter Conversation

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by

Danica Greetham

on 11 September 2013

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Transcript of On Twitter Conversation

We looked at the tweets from UK to UK having @ sign

If person A puts “@B” it designates that A is addressing the tweet to B specifically, but B is not the only one who can see the message (even people who do not follow A could see this message).
numbers
4.4M, 730k,
daily:
vertices: 66877 --- 109213 (87624),
edges: 123398 --200295 (158380)
data
© University of Reading 2012
On Twitter conversations
daily tweets number
An individual daily maximum -590;
for all 28 days - 5,604;
the mean daily tweet rate -0.9410.
clustering coefficient
Multi-user conversations
Balance
Identify multi-users exchanges;
Determine how many users typically engage in them;
Identify their time-frame and pace;
Calculate how balanced they are.
Multi-user conversations
'floor-gaining'
round robin
r=|E|/|V|
p=100*r/|E|
(indegree-outdegree)+/#mssgs
Determine how many users typically engage in them
Users involvement in different groups
Summary
9hrs magic number for distinct conversations
conversations are mostly fast-paced and balanced
multi-user exchanges can be found thanks to data sparsity, similar threshold of 9hrs
most exchanges dominated by 1 or 2 users
but also some evidence of balanced (up to 5 ) multi-user exchange
FFriday clustering coefficients visible
Mean maximum conversation contribution as a function of minimum contribution.
Distribution of time intervals between consecutive tweets from individual users
Conversational time-patterns
joint work with Jonathan Ward, University of Leeds
Danica Greetham,
University of Reading
``everything is about conversation and not about broadcasting''
"Those pauses, like the spaces between PowerPoint slides, become a metaphor for the gaps between what we mean and what we say"
Most of the previous work about information flow and retweeting...
Most of the previous network analysis focused on followers...

"A visit from the Goon Squad" by Jennifer Egan, 2011

Motivation
Mentions network - main characteristics
The data was collected by Datasift.
Aim
to describe main characteristics of Twitter "chatter": pace, timing, balance
d.v.greetham@reading.ac.uk
d.v.greetham@reading.ac.uk
Pace
Threshold
Identification
Pace
Balance
Thanks!
d.v.greetham@reading.ac.uk
Pairwise conversations
ABAB or AABB
Time difference threshold
Balance
Distribution of conversation balance
ratio
Conversation- subsequence with the contribution from all parties
~9hrs, obtained from the data
Fast...
Acknowledgments:
Colin Singleton from Counting Lab
Datasift
Horizon Hub EPSRC grant EP/G065802/1
Some references:
C. Honeycutt and S. C. Herring, “Beyond microblogging: Conversation
and collaboration via twitter,” in Proceedings of the Forty-Second
Hawai’i International Conference on System Sciences, Los Alamitos,
2009.
A. Ritter, C. Cherry, and B. Dolan, “Unsupervised modeling of twitter
conversations,” in HLT-NAACL 2010, 2010.
“Characterizing and curating conversation threads: expansion, focus,
volume, re-entry,” in Proceedings of WSDM ’13. New York, NY, USA: ACM, 2013, pp. 13–22.
Aims:
Full transcript