Paper by Axel Bruns, Tim Graham, and Dan Angus, presented at the Association of Internet Researchers conference, Brisbane, 5 Oct. 2019.

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AoIR 2019, Brisbane, 2-5 Oct. 2019
Axel Bruns, Tim Graham, Dan Angus
Trust Us, Again?
Twitter Campaigning Strategies in the 2019 Australian Federal Election

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Politics in Australia
● It’s … complicated:
(https://www.9news.com.au/national/australias-main-
national-sport-is-leadership-spills-according-to-
wikipedia/0b3e8ed0-d0e8-4522-863a-ce650a72706d)

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Politics in Australia
● Persistent instability:
● Six changes of Prime Minister since 2007
● Four of these through intra-party leadership challenges (‘spills’)
● Only two as a result of elections
(https://theconversation.com/australians-
trust-in-politicians-and-democracy-hits-
an-all-time-low-new-research-108161)

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Data and Methods
● Data:
● All tweets by and directed at (@mentions + retweets) all known House and
Senate candidate accounts on Twitter
● 22 April (close of nominations) to 17 May 2019 (day before election day)
● Same approach as 2013 (Bruns, 2017) and 2016 (Bruns & Moon, 2018)
● Analysis:
● Overall activity patterns (Axel)
● Themes of discussion (Dan)
● Presence of social bots (Tim)

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Overall Activity Patterns: Tweets Received

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Overall Activity Patterns: Tweets Sent

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Overall Activity Patterns: Inter-Party Tweeting

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Themes

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Themes (cont.)
Topic Description Topic Description
1 Scott Morrison 25 Coalition, chaos
24 Bill Shorten, mother, women 3 Liberal Party cuts to essential services (critical)
2 ClivePalmer, Labor 4 Water, Tax
10 Liberal party 7 Big business, cuts to jobs/economy
11 Greensparty 14 Tony Abbott, right politics, SSM, children in detention
15 Barnaby Joyce, 'giveaway'natural resources
5 Better future(optimistic) 19 Angus Taylor (critical), company director
9 Climatechange 21 Spending, inequality, 'top end', hospitals
13 PrimeMinister, election, polling, Kooyong
16 Media (critical), Policies 23 budget surplus, 'back in black'
22 campaign promises 6 Queensland coal communities
26 Pay workers, penalty rates, unions, retirees 17 Taxes
Politicians/parties
Policy and politics
Critical of Gov. (LIB)
Critical of Opp. (ALP) /
Supportive of Gov. (LIB)

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Themes over Time
Business/economy Campaign/promises Company/wages Tax Penalty rates / retirees

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Themes per Party
● [Dan to complete]

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Bots (or Highly Automated Accounts)
● Analysis of bot-like, highly automated accounts
● Election interference and political manipulation
● 2016 U.S. Election (Bessi & Ferrara, 2016, Rizoiu et al, 2018)
● Spreading fake news (Shao et al, 2018)
● 2017 French Presidential Election (Ferrara, 2017)
● Misdirecting online discussion of the Syrian civil war (Abokhodair et al, 2015)
● … and more
● Botometer classifier
● Bot score
● CAP score
● Caveats…

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Bot Presence During the Election
● 7,764 accounts (9%) with bot
score greater than 0.6
● But only 1,359 accounts (1.5%)
with CAP score > 0.8
● Volumes alone provide mixed
evidence about bot activity
● We define two populations of user
accounts (Rizoiu et al (2018):
● ‘Bot’ = botometer score > 0.6
● ‘Human’ = botometer score < 0.2
Bot-like range

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Newborn Bots…?

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Portrait of an Australian Election Bot

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What Do ‘Bots’ Talk About?
● Logistic regression - examine correlation between themes/topics and
CAP score (converted to binary variable)
● IVs: Topic probability vectors (from Dan’s analysis)
● DV: CAP score binary (human < 0.2; bot > 0.8)
Category Topic/theme Coefficient
estimate
P-value
Policy and politics 9 – Climate Change -3.37442 0.004925
**
Critical of Gov. (LIB) 3 – Liberal party cuts
to essential services
-3.88255 0.000392
***
Critical of Gov. (LIB) 4 – Water, Tax -3.01005 0.001541
**
Critical of Gov. (LIB) 7 – Big business,
cuts to jobs/economy
-4.57766 1.24e-08
***
Critical of Opp. (ALP)
/
Supportive of Gov.
(LIB)
6 – QLD Coal
Companies
4.21785 6.01e-09
***
● Findings
● Bots much less likely to be
critical of the government
(LIB)
● Bots much less likely to
discuss climate change
● Bots much more likely to be
supportive of QLD coal
companies

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Some Preliminary Conclusions
● Dynamics:
● Many more retweets for opposition parties (but not necessarily the leaders)
● Clear focus on Liberal Party within the conservative Coalition
● Clear distinctions in media content being shared
● United Australia Party ran separate interference campaign
● Topics:
● Diversity of online discourses – there is no single ‘big’ election-defining issue
● Bots:
● Bots distribution by topics suggests that bots are preferentially targeting specific
topics
● Bots appear to be partisan
● Surge of bot accounts created before the election?
● Bots were quite active but it is unclear how much impact they had

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AoIR 2019, Brisbane, 2-5 Oct. 2019
Axel Bruns, Tim Graham, Dan Angus
@snurb_dot_info – Axel Bruns
@antmandan – Dan Angus
@timothyjgraham – Tim Graham
@socialmediaQUT – http://socialmedia.qut.edu.au/
@qutdmrc – https://www.qut.edu.au/research/dmrc
This research is supported by the ARC Future Fellowship project
“Understanding Intermedia Information Flows in the Australian
Online Public Sphere”, the ARC Discovery project “Journalism
beyond the Crisis: Emerging Forms, Practices, and Uses”, and the
ARC LIEF project “TrISMA: Tracking Infrastructure for Social
Media Analysis.”

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Abokhodair, N., Yoo, D., & McDonald, D. W. (2015). Dissecting a social botnet: Growth, content and influence in Twitter. Paper
presented at the Proceedings of the 18th ACM Conference on Computer Supported Cooperative Work & Social Computing (CSCW),
Vancouver, Canada, 839–851.
Bessi, A., & Ferrara, E. (2016). Social bots distort the 2016 us presidential election online discussion. First
Monday, 21(11). https://doi.org/10.5210/fm.v21i11.7090
Ferrara, E. (2017). Disinformation and social bot operations in the run up to the 2017 French presidential election. First Monday,
22(8). https://doi.org/10.5210/fm.v22i8.8005
Rizoiu, M. A., Graham, T., Zhang, R., Zhang, Y., Ackland, R., & Xie, L. (2018, June). # DebateNight: The Role and Influence of
Socialbots on Twitter During the 1st 2016 US Presidential Debate. In Twelfth International AAAI Conference on Web and Social
Media.
Shao, C., Ciampaglia, G. L., Varol, O., Yang, K.‐C., Flammini, A., & Menczer, F. (2018). The spread of low‐credibility content by social
bots. Nature Communications, 9(1), 4787.