Social Media in Australia

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

Prof. Axel BrunsARC Future FellowDigital Media Research CentreQueensland University of [email protected] – @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

https://www.qut.edu.au/research/dmrc

https://www.qut.edu.au/research/dmrc

Journalism, Public Communication & Democracy

Economies, Policies & Regulation

Digital Methods

Technol

ogies

&

Prac

tices i

n Every

day 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

Global: Steady Growth?

Australia: Saturation Point?

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 accountsNetwork of follower connections

Filtered for degree ≥1000255k nodes (6.4%), 61m edges

Edges not shown in graph

Clusters

Louvain Modularity Resolution: 1.0

Clusters


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 accountsNetwork of follower connections

Filtered for degree ≥1000255k 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

SouthAustralia

Celebrities

Horse Racing

2006

Year of account creationRed: new / yellow: past

2007


2008


2009


2010


2011


2012


2013


2014


2015


Changing Demographics

Protected Accounts

Red: true / yellow: false

Verified Accounts (2.84%)

Red: true / yellow: false

Included in Twitter Lists (4.37%)

Colour scale: yellow to redMaximum: 44k lists

Number of Tweets Faved/Liked

Colour scale: yellow to redMaximum: 551k faves

Total Number of Tweets Posted

Colour scale: yellow to redMaximum: 1.1m tweets

Tweets Posted (Q1/2017)

Colour scale: yellow to redMaximum: 96k tweets

No Tweets (Q1/2017)

Non-tweeting accounts in red(includes protected accounts)

45% of all 255k accounts

Tweets per Cluster (Average)

Colour scale: yellow to redNon-tweeting accounts in grey

Louvain modularity resolution 0.5Average over tweeting accounts only

Tweets per Cluster (Average)

Colour scale: yellow to redNon-tweeting accounts in grey

Louvain modularity resolution 0.25Average over tweeting accounts only

Tweet Types (Q1/2017)

Colours:Purple: 50%+ original tweets

Orange: 50%+ @mentionsGreen: 50%+ retweets

Grey: balanced mix

Hashtags (Q1/2017)

Prominent Hashtags (Q1/2017)



#auspol

Red: hashtag used in Q1/2017506k tweets from 13k accounts

#ausopen


#trump


‘Trump’

Red: hashtag used in Q1/20171.5m 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:

– Scale from +1 (100% external) to -1 (100% internal)

E-I Index per Cluster

Colour scale: red (-1) to green (+1)Louvain modularity resolution 0.5Minimum: -0.95 / Maximum: 0.52

E-I Index per Account

Colour scale: red (-1) to green (+1)Louvain modularity resolution 0.5

Minimum: -1 / Maximum: 1

E-I Index per Cluster

Colour scale: red (-1) to green (+1)Louvain modularity resolution 0.25Minimum: -0.97 / Maximum: 0.92

E-I Index per Account

Colour scale: red (-1) to green (+1)Louvain modularity resolution 0.25

Minimum: -1 / Maximum: 1

E-I Index Distribution

(Box plots show middle 50% of the data points in each cluster.)

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.