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One Day in the Life of a National Twittersphere

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Paper by Axel Bruns and Brenda Moon, presented at Social Media and Society, London, 13 July 2016.

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One Day in the Life of a National Twittersphere

  1. 1. One Day in the Life of a National Twittersphere Prof. Axel Bruns & Dr. Brenda Moon Digital Media Research Centre Queensland University of Technology Brisbane, Australia a.bruns@qut.edu.au | brenda.moon@qut.edu.au @snurb_dot_info | @brendam Axel Bruns is currently a visiting scholar at the Alexander-von-Humboldt-Institut for Internet and Society, Berlin.
  2. 2. Twitter: State of the Field • 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
  3. 3. 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) • Filtering by description, location, timezone fields • Focus on identifiably Australian cities, states, timezones and other markers • 2.8 million Australian accounts identified (by Sep. 2013) • Retrieval of their follower/followee lists • Best guess of account location based on timezone, location and description settings
  4. 4. Education Agriculture Literature Adelaide / SA Food Wine Beer Parenting Mums PR Netizens Marketing Investing Real Estate Home Business Sole Traders Self-Help HR / Support Followback Urban Media Utilities Advertising Business Fashion Beauty Arts Cinema Journalists Politics Hard RightLeftists News CyclingTalkback Music TV V8s UFC NRL AFL Football Horse Racing Cricket NRU Celebrities Hillsong Perth Pop Media Teen Idols Cody Simpson The Australian Twittersphere 2.8m known Australian accounts Network of follower connections Filtered for degree ≥1000 140k nodes (~5%), 22.8m edges Labels assigned through qualitative evaluation
  5. 5. TrISMA: Tracking Australian Twitter • ARC LIEF project: – Tracking Infrastructure for Social Media Analysis – Multi-university project led by QUT to develop comprehensive infrastructure for large- scale social media data analytics – Twitter: continuous capture of tweets by all 2.8m identified Australian accounts – 1b+ tweets captured to date, 1m+ new tweets/day – Data storage via Google BigQuery, analysis via Tableau and Gephi • Basic conventions: – All dates in AEST (UTC+10: Sydney, Melbourne, Brisbane); other cities up to two hours behind – Tweet types: original, @mention, retweet (RT/MT/HT/via/"@user or retweet button) – Hashtags: A-Z + 0-9 + _, and at least three characters
  6. 6. Twitter in Australia: Overall Patterns 2015 Video Music Awards unique accounts
  7. 7. Twitter in Australia: Overall Patterns
  8. 8. Twitter in Australia: Overall Patterns
  9. 9. ONE (RANDOM) DAY: 6 AUG. 2015 (Work in progress!)
  10. 10. Overall Patterns: 6 Aug. 2015
  11. 11. 1.1m tweets from 147k, to 224k accounts 294k nodes total, including non-Australians 535k edges from 856k @mentions / RTs Visualisation: Gephi, Force Atlas 2 Node colours: Gephi, modularity resolution 1.0
  12. 12. 1.1m tweets from 147k, to 224k accounts 294k nodes total, including non-Australians 535k edges from 856k @mentions / RTs Visualisation: Gephi, Force Atlas 2 Edge colours: red = @mentions, green = retweets
  13. 13. 1.1m tweets from 147k, to 224k accounts 294k nodes total, including non-Australians 535k edges from 856k @mentions / RTs Visualisation: Gephi, Force Atlas 2 Mutual edges only: 37k edges (7.05% of total) Edge colours: red = @mentions, green = retweets
  14. 14. 1.1m tweets from 147k, to 224k accounts 294k nodes total, including non-Australians 535k edges from 856k @mentions / RTs Visualisation: Gephi, Force Atlas 2 Colours: Gephi, modularity resolution 1.0 Labels assigned through qualitative evaluation Politics Cricket Teen Culture Pop
  15. 15. Tweeting Activities per Cluster
  16. 16. Hashtags per Cluster
  17. 17. Account Positions in Follower Network
  18. 18. Cluster Interactions
  19. 19. E-I Index (𝐸𝑥𝑡𝑒𝑟𝑛𝑎𝑙 − 𝐼𝑛𝑡𝑒𝑟𝑛𝑎𝑙 𝐸𝑑𝑔𝑒𝑠 𝑇𝑜𝑡𝑎𝑙 𝐸𝑑𝑔𝑒𝑠 ) EII = -1: only internal edges / EII = +1: only external edges
  20. 20. First Observations • Crucial to move Twitter studies beyond hashtag studies: – Majority of activity outside of hashtags – Follow-on conversations especially important • Follower and interaction networks intersect: – Significant account interactions across follow network cluster boundaries – Some clusters more than others, some topics more than others (and ‘clusters’ are soft constructs) – Dynamics of such processes still to be understood fully • Significant differences across Twitter communities: – Inward vs. outward orientation – Retweet vs. @mention interactions – Mutual interaction vs. retweeting of few key accounts • Different cultures of Twitter use: – Potentially related to age and experience of Twitter users (e.g. older politics-focussed accounts vs. younger teen accounts)
  21. 21. 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, and was conducted while visiting the Alexander-von-Humboldt-Institut for Internet and Society.

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