Social Media in Australia: A ‘Big Data’ Perspective on Twitter

Social Media in Australia:
A ‘Big Data’ Perspective on Twitter
Prof. Axel Bruns
ARC Future Fellow
Digital Media Research Centre
Queensland University of Technology
a.bruns@qut.edu.au – @snurb_dot_info

QUT Digital Media Research Centre
The Digital Media Research Centre (DMRC)
conducts world-leading research that helps society
understand and adapt to the social, cultural and
economic transformations associated with digital
media technologies, and trains the researchers of
tomorrow.
For more, see: http://www.qut.edu.au/research/dmrc

Journalism, Public
Communication &
Democracy
Economies, Policies
& Regulation
Digital Methods
Technologies &
Practices in
Everyday Life
DIGITAL
MEDIA
DMRC PROGRAMMES

Research Project
• ARC Future Fellowship:
– Four-year project
– Axel Bruns (FF), Brenda Moon (Postdoc),
Felix Münch (PhD1, 2014-2017), Ehsan Dehghan (PhD2, 2016-2018)
At the intersection of mainstream, niche, and social media, the processes by
which public opinion forms and public debate unfolds are increasingly
complex, and poorly understood. This project draws on large datasets and
innovative methods to develop a new model of the Australian online public
sphere.
• Also supported by ARC LIEF project:
– Two-year project (2014/15; QUT, Curtin, Deakin, Swinburne) to develop
comprehensive infrastructure for large-scale social media data analytics

The Australian Twittersphere
• Twitter in Australia:
– Strong take-up since 2009
– Centred around 25-55 age range, urban, educated, affluent users (but gradually broadening)
– Significant role in crisis communication, political communication, audience engagement, …
• Mapping the Twittersphere:
– Long-term project to identify all Australian Twitter accounts
– First iteration: snowball crawl of follower/followee networks
• Starting with key hashtag populations (#auspol, #spill, …)
• Map of ~1m accounts in early 2012
– Second iteration: full crawl of global Twitter ID numberspace through to Sep. 2013 (~870m accounts)
– Third iteration: full crawl of global Twitter ID numberspace through to Feb. 2016 (~1.4b accounts)
• Filtering by description, location, timezone fields: identifiably Australian cities, states, timezones, etc.
• 4 million Australian accounts identified (by Feb. 2016)
• Retrieval of their follower/followee lists
– Continuous gathering of their public tweets
• Capturing ~1.3m new tweets per day

Why are we doing this?
• Twitter research to date:
– Abundance of hashtag studies: volumetrics, keywords, networks, …
– Some studies profiling samples of the total userbase (e.g. celebrities, politicians)
– Some comprehensive (?) tracking of activities around key events and topics
– Some egocentric follower network maps, largely small-scale
– Almost absent: comprehensive follower network maps, longitudinal userbase development trajectories, user career
patterns from sign-up to listener/celebrity/…
• The political economy of Twitter research:
– Twitter API data access is shaped to privilege certain approaches
– Research funding is easier to obtain for specific, limited purposes
– Longitudinal, ‘big’ data access requires ongoing, substantial funding and infrastructure
– Exploratory, data-driven research is difficult to sell to most funding bodies
– Also related to divergent resources available to different scholarly disciplines
 Most ‘hard data’ Twitter research conducted by Twitter, Inc. and commercial research institutes

Mapping the Australian Userbase
• Mapping the Twittersphere:
– Filtered to include only accounts with (followers + followees) >= 1000
• ~255k accounts, 61m follower/followee connections within this group
– Mapped using Gephi Force Atlas 2 algorithm (LinLog mode, scaling 0.00001, gravity 1.0)
• Force-directed visualisation: closely interconnected groups of accounts will form clusters in the network
• Clusters in the Twittersphere:
– Identification of clusters using the Louvain community detection algorithm (resolutions 0.5 and 0.25)
– Qualitative interpretation of clusters themes based on high-degree nodes in each cluster
• Applications:
– Combined analysis of network structures and tweeting activities
– Evaluation of potential and actual information flows across the network
– Comparative benchmarking of clusters across different markers

The Australian Twittersphere, 2016
4m known Australian accounts
Network of follower connections
Filtered for degree ≥1000
255k nodes (6.4%), 61m edges
Edges not shown in graph

Clusters
Louvain Modularity Resolution: 1.0

Clusters

Clusters: Teen Culture (61k)

Clusters: Aspirational (26k)

Clusters: Sports (22k)

Clusters: Netizens (17k)

Clusters: Miscellaneous (15k)

Clusters: Arts & Culture (12k)

Clusters: Politics (12k)

Clusters: Television & Fashion (12k)

Clusters: Popular Music (11k)

Clusters: Food and Drinks (10k)

Clusters: Travel (7k)

Clusters: Activism & Charities (6k)

Clusters: Queensland (5k)

Clusters: Western Australia (5k)

Clusters: Victoria (4k)

Clusters: Porn (4k)

Clusters: Business News (3k)

Clusters: LGBTIQ (3k)

Clusters: Education (2k)

Clusters: Cycling (2k)

4m known Australian accounts
Network of follower connections
Filtered for degree ≥1000
255k nodes (6.4%), 61m edges
Edges not shown in graph
Clusters
Teen Culture
Aspirational
Sports
Netizens
Arts & Culture
Politics
Television
Fashion
Popular Music
Food & Drinks
Agriculture Activism
Porn
Education
Cycling
News &
Generic
Hard Right
Progressive
South
Australia
Celebrities
Horse Racing

2006
Year of account creation
Red: new / yellow: past

2007

2008

2009

2010

2011

2012

2013

2014

2015

Protected Accounts
Red: true / yellow: false

Verified Accounts (2.84%)
Red: true / yellow: false

Included in Twitter Lists (4.37%)
Colour scale: yellow to red
Maximum: 44k lists

Number of Tweets Faved/Liked
Maximum: 551k faves

Total Number of Tweets Posted
Maximum: 1.1m tweets

Tweets Posted (Q1/2017)
Maximum: 96k tweets

No Tweets (Q1/2017)
Non-tweeting accounts in red
(includes protected accounts)
45% of all 255k accounts

Tweets per Cluster (Average)
Non-tweeting accounts in grey
Louvain modularity resolution 0.5
Average over tweeting accounts only

Tweets per Cluster (Average)
Non-tweeting accounts in grey
Average over tweeting accounts only

Tweet Types (Q1/2017)
Colours:
Purple: 50%+ original tweets
Orange: 50%+ @mentions
Green: 50%+ retweets
Grey: balanced mix

#auspol
Red: hashtag used in Q1/2017
506k tweets from 13k accounts

#ausopen

#trump

‘Trump’
1.5m tweets from 44k accounts

#womensmarch

#qanda

#notmydebt

#bbl06

#melbourne

Echo Chambers
• How exclusive are the clusters?
– Strongly inwardly focussed = echo chamber
– Strongly outwardly focussed = information hubs
• Possible measure: Krackhardt E-I Index
– Difference of external and internal links as proportion of total:
𝐸−𝐼 𝐼𝑛𝑑𝑒𝑥 =
# 𝐸𝑥𝑡𝑒𝑟𝑛𝑎𝑙 𝐿𝑖𝑛𝑘𝑠 − # 𝐼𝑛𝑡𝑒𝑟𝑛𝑎𝑙 𝐿𝑖𝑛𝑘𝑠
# 𝐸𝑥𝑡𝑒𝑟𝑛𝑎𝑙 𝐿𝑖𝑛𝑘𝑠 + # 𝐼𝑛𝑡𝑒𝑟𝑛𝑎𝑙 𝐿𝑖𝑛𝑘𝑠
– Scale from +1 (100% external) to -1 (100% internal)

E-I Index per Cluster
Colour scale: red (-1) to green (+1)
Minimum: -0.95 / Maximum: 0.52

E-I Index per Account
Minimum: -1 / Maximum: 1

E-I Index per Cluster
Minimum: -0.97 / Maximum: 0.92

E-I Index per Account
Minimum: -1 / Maximum: 1

E-I Index Distribution
(Box plots show middle 50% of the data points in each cluster.)

http://mappingonlinepublics.net/
@snurb_dot_info
@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.

Social Media in Australia: A ‘Big Data’ Perspective on Twitter

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