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

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Invited presentation at the University of Melbourne, 4 April 2017.

Twitter research to date has focussed mainly on the study of isolated events, as described for example by specific hashtags or keywords relating to elections, natural disasters, public events, and other moments of heightened activity in the network. This limited focus is determined in part by the limitations placed on large-scale access to Twitter data by Twitter, Inc. itself. This research presents the first ever comprehensive study of a national Twittersphere as an entity in its own right. It examines the structure of the follower network amongst some 4 million Australian Twitter accounts and the dynamics of their day-to-day activities, and explores the Australian Twittersphere’s engagement with specific recent events.

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

  1. 1. 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
  2. 2. 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
  3. 3. Journalism, Public Communication & Democracy Economies, Policies & Regulation Digital Methods Technologies & Practices in Everyday Life DIGITAL MEDIA DMRC PROGRAMMES
  4. 4. 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
  5. 5. 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
  6. 6. 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
  7. 7. Global: Steady Growth?
  8. 8. Australia: Saturation Point?
  9. 9. 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
  10. 10. 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
  11. 11. Clusters Louvain Modularity Resolution: 1.0
  12. 12. Clusters Louvain Modularity Resolution: 0.5
  13. 13. Clusters Louvain Modularity Resolution: 0.25
  14. 14. Clusters: Teen Culture (61k) Louvain Modularity Resolution: 0.5
  15. 15. Clusters: Aspirational (26k) Louvain Modularity Resolution: 0.5
  16. 16. Clusters: Sports (22k) Louvain Modularity Resolution: 0.5
  17. 17. Clusters: Netizens (17k) Louvain Modularity Resolution: 0.5
  18. 18. Clusters: Miscellaneous (15k) Louvain Modularity Resolution: 0.5
  19. 19. Clusters: Arts & Culture (12k) Louvain Modularity Resolution: 0.5
  20. 20. Clusters: Politics (12k) Louvain Modularity Resolution: 0.5
  21. 21. Clusters: Television & Fashion (12k) Louvain Modularity Resolution: 0.5
  22. 22. Clusters: Popular Music (11k) Louvain Modularity Resolution: 0.5
  23. 23. Clusters: Food and Drinks (10k) Louvain Modularity Resolution: 0.5
  24. 24. Clusters: Travel (7k) Louvain Modularity Resolution: 0.5
  25. 25. Clusters: Activism & Charities (6k) Louvain Modularity Resolution: 0.5
  26. 26. Clusters: Queensland (5k) Louvain Modularity Resolution: 0.5
  27. 27. Clusters: Western Australia (5k) Louvain Modularity Resolution: 0.5
  28. 28. Clusters: Victoria (4k) Louvain Modularity Resolution: 0.5
  29. 29. Clusters: Porn (4k) Louvain Modularity Resolution: 0.5
  30. 30. Clusters: Business News (3k) Louvain Modularity Resolution: 0.5
  31. 31. Clusters: LGBTIQ (3k) Louvain Modularity Resolution: 0.5
  32. 32. Clusters: Education (2k) Louvain Modularity Resolution: 0.5
  33. 33. Clusters: Cycling (2k) Louvain Modularity Resolution: 0.5
  34. 34. 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
  35. 35. 2006 Year of account creation Red: new / yellow: past
  36. 36. 2007 Year of account creation Red: new / yellow: past
  37. 37. 2008 Year of account creation Red: new / yellow: past
  38. 38. 2009 Year of account creation Red: new / yellow: past
  39. 39. 2010 Year of account creation Red: new / yellow: past
  40. 40. 2011 Year of account creation Red: new / yellow: past
  41. 41. 2012 Year of account creation Red: new / yellow: past
  42. 42. 2013 Year of account creation Red: new / yellow: past
  43. 43. 2014 Year of account creation Red: new / yellow: past
  44. 44. 2015 Year of account creation Red: new / yellow: past
  45. 45. Changing Demographics
  46. 46. Protected Accounts Red: true / yellow: false
  47. 47. Verified Accounts (2.84%) Red: true / yellow: false
  48. 48. Included in Twitter Lists (4.37%) Colour scale: yellow to red Maximum: 44k lists
  49. 49. Number of Tweets Faved/Liked Colour scale: yellow to red Maximum: 551k faves
  50. 50. Total Number of Tweets Posted Colour scale: yellow to red Maximum: 1.1m tweets
  51. 51. Tweets Posted (Q1/2017) Colour scale: yellow to red Maximum: 96k tweets
  52. 52. No Tweets (Q1/2017) Non-tweeting accounts in red (includes protected accounts) 45% of all 255k accounts
  53. 53. Tweets per Cluster (Average) Colour scale: yellow to red Non-tweeting accounts in grey Louvain modularity resolution 0.5 Average over tweeting accounts only
  54. 54. Tweets per Cluster (Average) Colour scale: yellow to red Non-tweeting accounts in grey Louvain modularity resolution 0.25 Average over tweeting accounts only
  55. 55. Tweet Types (Q1/2017) Colours: Purple: 50%+ original tweets Orange: 50%+ @mentions Green: 50%+ retweets Grey: balanced mix
  56. 56. Hashtags (Q1/2017)
  57. 57. Prominent Hashtags (Q1/2017)
  58. 58. Prominent Hashtags (Q1/2017)
  59. 59. Prominent Hashtags (Q1/2017)
  60. 60. #auspol Red: hashtag used in Q1/2017 506k tweets from 13k accounts
  61. 61. #ausopen Red: hashtag used in Q1/2017 60k tweets from 8k accounts
  62. 62. #trump Red: hashtag used in Q1/2017 57k tweets from 8k accounts
  63. 63. ‘Trump’ Red: hashtag used in Q1/2017 1.5m tweets from 44k accounts
  64. 64. #womensmarch Red: hashtag used in Q1/2017 57k tweets from 11k accounts
  65. 65. #qanda Red: hashtag used in Q1/2017 49k tweets from 5k accounts
  66. 66. #notmydebt Red: hashtag used in Q1/2017 42k tweets from 4k accounts
  67. 67. #bbl06 Red: hashtag used in Q1/2017 38k tweets from 3k accounts
  68. 68. #melbourne Red: hashtag used in Q1/2017 37k tweets from 7k accounts
  69. 69. 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)
  70. 70. E-I Index per Cluster Colour scale: red (-1) to green (+1) Louvain modularity resolution 0.5 Minimum: -0.95 / Maximum: 0.52
  71. 71. E-I Index per Account Colour scale: red (-1) to green (+1) Louvain modularity resolution 0.5 Minimum: -1 / Maximum: 1
  72. 72. E-I Index per Cluster Colour scale: red (-1) to green (+1) Louvain modularity resolution 0.25 Minimum: -0.97 / Maximum: 0.92
  73. 73. E-I Index per Account Colour scale: red (-1) to green (+1) Louvain modularity resolution 0.25 Minimum: -1 / Maximum: 1
  74. 74. E-I Index Distribution (Box plots show middle 50% of the data points in each cluster.)
  75. 75. E-I Index Distribution (Box plots show middle 50% of the data points in each cluster.)
  76. 76. 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.

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