This document details a study on reply chains in the Australian Twittersphere, identifying 383 million tweets as part of reply trees, with various metrics such as reply tree size and unique reply targets analyzed. The research highlights the limitations of the dataset, noting 65% of replied-to tweets are not in the dataset, primarily focusing on Australian accounts. Future work includes further analysis of reply trees to distinguish conversational patterns and improve social media discourse analysis.

1. CHARACTERISING TWITTER REPLY CHAINS IN
THE AUSTRALIAN TWITTERSPHERE
Brenda Moon
Digital Media Research Centre
Queensland University of Technology
brenda.moon@qut.edu.au, @brendam

2. Reply trees or reply cascades

3. Reply tree with all tweets in TrISMA
dataset

4. Reply tree with missing tweet

5. Reply tree with tweets outside
TrISMA dataset

6. Finding reply trees – previous approach
Seed tweet in_reply_to
tweet_id
in_reply_to
tweet_id
follow reply
chain back in
time using
‘in_reply_to’
field which
contains the
tweet_id of the
replied to
tweet
searching for
tweet_id in
‘in_reply_to’
field

7. Finding reply trees – new approach
• Set a unique reply_tree_id for each tweet in a reply tree by
working through all tweets in dataset in time order starting
from oldest
• reply_tree_id is set to the tweet_id of the originating tweet
(originating tweets don’t have in_reply_to set) if it is in the
TrISMA dataset, otherwise to the in_reply_to field of the
oldest tweet in the reply tree in the TrISMA dataset.
• The reply_tree_id may be a tweet id that is outside the
TrISMA dataset.
• For each reply tweet, can find reply_tree_id by looking up the
tweet it is in reply.

8. Results based on new approach
Identified
• 383 million tweets as being part of reply trees in the
whole TrISMA dataset (20.4% of all tweets)
– Of these, 56 million (14.6%) are replying to tweets inside
the TrISMA dataset and 323 million are replying to external
tweets
• These reply to 322 million unique tweets
– of which 47 million (14.6%) are in the TrISMA dataset and
275 million are external.

9. Histogram of reply tree sizes

10. Structure of reply trees: single layer vs
multiple layers
• Using ratio of reply tweets to reply target tweets as
indicator.
– 1/n (approaching 0) is single layer – all tweets replying to
single original tweet
– 1 is linear – each tweet replies to previous one in the tree

11. Ratio of unique reply targets to total replies by reply tree size
(count of in_reply_to)
(largest trees filtered, min tree size 10)

12. Largest reply tree
• Largest reply tree has 11,656
tweets replying to 41 different
tweets – nearly all the tweets reply
to an original tweet by a celebrity
(original tweet has been deleted
and is outside TrISMA dataset)
• single layer tree
• Replies like:
– @NiallOfficial can't wait it's amazing!!
– @NiallOfficial follow me please!!!!
– @NiallOfficial follow me!

13. Reply tree with most unique replies
• A single conversation between two accounts with
230 tweets has the most unique reply targets of all
the reply trees
• There are 180 unique reply targets
• The conversation continues from 30/8/2010 to
27/10/2010
• Looks like a private conversation, range of day to day
topics.

14. Limitations
• The initial results show that 65% of the replied-to tweets
are not in the TrISMA dataset, which is limited to tweets
from Australian accounts. Although it is possible to
retrieve these tweets using the Twitter API to find the
older tweets in the reply tree, it is not possible to go the
other way and find external tweets that reply to tweets
in the TrISMA dataset except by manually using the
Twitter client interface.
• I focus on the Australian Twittersphere so I exclude these
external tweets, even though that will reduce the lengths
of some of the reply chains.

15. Further work
Now that all the reply trees in the TrISMA database have been
identified it is possible to:
• continue with the analysis of the reply trees, looking at different
metrics for reply trees and variations over time.
• evaluate the utility of reply tree metrics in categorising different
conversational patterns. This may make it possible to
computationally distinguish types of conversation, for example
single-authored threads, rapid and heated arguments, extended
chats, multi-user responses to a single controversial tweet.
• apply the reply trees for large-scale social media discourse analysis
and in computational filtering of ‘big social data’ into smaller
samples for analysis.

16. http://mappingonlinepublics.net/
@brendam
@socialmediaQUT – http://socialmedia.qut.edu.au/
@qutdmrc – https://www.qut.edu.au/research/dmrc
This research is funded by the Australian Research Council through Future Fellowship and LIEF grants FT130100703 and
LE140100148.