Paper by Brenda Moon presented at the Social Media & Society International Conference 2017 in Toronto, Canada. WIP Paper is available here: http://bit.ly/SMSociety_AU_Bots

1. Identifying Bots in the Australian Twittersphere
Dr Brenda Moon
@brendam
WIP Paper DOI: 10.1145/3097286.3097335

2. @brendam
What is a Twitter bot?
• Twitter account which uses automation
– Automation of tweets
– Automation of follow / unfollow

3. @brendam
What is a Twitter bot?
• Tools like Hootsuite support automation for accounts
where content is still written by a human – allowing
delayed posting and automatically following back
people who follow the account
• Need to consider where the boundary is between
Human/ Cyborg / Bot – may depend on what you are
studying

4. @brendam
Transition from human to bot
Increasing automation
Human BotCyborg

5. @brendam
Bestiary of Bots
Mowbray (2014) proposes a typology of bots – an
overlapping classification including :
• marketing bots
• useful bots
• entertaining bots
• the internet of toasters
• antisocial bots
• do not look like a bot

7. @brendam
Australian Twittersphere
• TrISMA ARC LIEF project:
– Tracking Infrastructure for Social Media Analysis (Bruns, Burgess
& Banks et al., 2016).
– Multi-university project led by QUT to develop comprehensive
infrastructure for large-scale social media data analytics
– Twitter: continuous capture of tweets by all identified Australian
accounts
– 2.3b+ tweets captured to date, 1m+ new tweets/day
– Data storage in Google BigQuery, analysis via tools like Tableau,
Gephi and Python
• 2.8 million identified Australian accounts in 2013
• 4 million identified Australian accounts in 2016

8. @brendam
Australian Twittersphere
• Identify which of the 4 million Australian accounts
are bots
• Expect to find less than the 8.5% to 16% identified in
other datasets because bots may not have the time
zone, location or description information we use to
select dataset
• Interested in studying bot behaviour, not excluding
them

9. @brendam
Initial Results
Work in progress!

10. @brendam
Account names ending with ‘bot’
• 1,062 accounts with screen_name ending in ‘bot’
(lower case comparison)
• On inspection of these, some seem to be surnames:
– abbot, barbot, cobot, talbot, aglibot, cabot, rowebot,
tembot, babot, chabot, tabbot, wolbot
– Reviewer suggested Rowebot might be ”Robot” but I
double checked that account and it looks like a person
• 908 accounts remaining after these were removed
(some may still not be bots)

11. Top 10 accounts ending in ‘bot’ by
number of tweets

12. @brendam
Using ’source’ meta data field in
tweets
• Source ’twifarm’ (used by Hegelich & Janetsko (2016)) –
only 22 accounts & 4,222 tweets (very low)
• Source ‘bot’ in source:
– 497 unique sources,
– removed 5 client apps that happen to have ’bot’ in name, e.g.
tapbots clients
– leaving 492 unique sources used by 2,882 accounts to send 4.9
million tweets
– 0.27% of total tweets. Of these 0.21% are contributed by bots
identifying as based on twittbot.net software.

15. @brendam
Outlier cluster – seems to be bots
• Around the edges of the cluster graph I’ve shown you
are a number of disconnected clusters.
• The two largest of these (still small – only 994
accounts in total) seem to consist of a cluster of bots
and another cluster of accounts linked to a game wiki
that might be either bot or human driven.
• Still investigating these

16. @brendam
What next?
• Very few accounts identified so far…
• Use botometer to check the accounts I’ve identified
as bots - was botornot (Davis et al, 2016)
• See if botometer can be used to check all of our
accounts – new API looks promising
• Apply other approaches –
– Patterns in accession of accounts
– Patterns in frequency of tweets
– Similarity of descriptions / names / images / tweets

17. @brendam
Questions or suggestions?
Work in Progress paper available here:
http://bit.ly/SMSociety_AU_Bots

18. @brendam
References
• Bruns, A., Burgess, J., Banks, J., Tjondronegoro, D., Dreiling, A.,
Hartley, J., … Sadkowsky, T. (2016). TrISMA: Tracking Infrastructure
for Social Media Analysis. Retrieved from http://trisma.org/
• Davis, C. A., Varol, O., Ferrara, E., Flammini, A. and Menczer. F. 2016.
BotOrNot: A System to Evaluate Social Bots. arXiv : 1602.009
(2016), 2. Retrieved from http://arxiv.org/abs/1602.00975
• Hegelich S. and Janetzko. D. 2016. Are social bots on twitter political
actors? Empirical evidence from a Ukrainian social botnet. Proc.
Tenth AAAI Conf. Web Soc. Media, ICWSM (2016), 579–582.
• Mowbray, M. (2014). "Automated Twitter Accounts." in Twitter and
Society (2014): 183-94. (K. Weller, A. Bruns, J. Burgess, M. Mahrt, &
C. Puschmann, Eds.)