Mapping Online Publics: New
Methods for Twitter Research
Axel Bruns, Jean Burgess, and Darryl Woodford
ARC Centre of Excel...
SOCIAL MEDIA RESEARCH AND ‘BIG DATA’
• Social media as the ‘big data’ moment in HASS
research
• But ‘big data’ + ‘social m...
Scott A. Golder*,
Michael W. Macy (2011)
Diurnal and Seasonal
Mood Vary with Work,
Sleep, and Day length
Across Diverse Cu...
SOCIAL MEDIA RESEARCH AND ‘BIG DATA’
• ‘Computational turn’ in new humanities research: shift
from computational tools to ...
THE MAPPING ONLINE PUBLICS PROJECT
• Australian Research Council (ARC) Discovery Project (2010-13) – $410,000 +
ARC Centre...
TWITTER AND ITS PUBLICS
HASHTAG PUBLICS: #QLDFLOODS (2011)
10 Jan 2011 11 Jan 2011 12 Jan 2011 13 Jan 2011 14 Jan 2011 15 Jan 2011
HASHTAG PUBLICS: #EGYPT @MENTIONS
1-28 Feb. 2011 15 June to 15 Sep. 2011
@mentions between users tweeting predominantly in...
PERSONAL PUBLICS
• How do personal publics (cf. Jan Schmidt) around social media
accounts form and dissolve?
– No clear da...
FOLLOWER ACCESSION
@TONYABBOTTMHR’S FAKE FOLLOWERS
(11/12 Aug. 2013)
TWITTER AND THE MEDIA ECOLOGY
TWITTER AND TV: TELEMETRICS
• The Importance of Hype
• Social Media Audiences
• From Sabermetric to Telemetrics
• The Hypo...
THE IMPORTANCE OF HYPE
• Traditional ratings measure
what people have watched, but
have limited impact on what
people *wil...
SOCIAL MEDIA RATINGS
• Contemporary commercial social media ratings have the same limitation,
even if you trust their numb...
BUT YOU NEED TO GET IT RIGHT
Beamly Screenshot: 31 May 2014
BUT YOU NEED TO GET IT RIGHT
Beamly Screenshot: 31 May 2014
BIG BROTHER USA VS AU (AUDIENCE)
• In US, for Big Brother (&
shows generally), there is little
correlation between viewers...
SEASONAL MODELS • Blue Line represents the
ratio of total viewers,
Orange Line represents ratio
of tweets (to season avera...
THE HYPOMETERTM
• iOS app developed as functional prototype
to act as a ‘modern TV Guide’ for Australian
television
• Calc...
SOME EARLY DATA
Underpromoted
Overperforming
High performingUnderperforming
TWITTER AND THE NEWS: ATNIX
• Australian Twitter News Index (ATNIX):
– Long-term project to track all tweets sharing links...
TWITTER: THE BIG PICTURE
TWITTER IN AUSTRALIA (AND BEYOND)
• Putting Twitter into context:
– Do specific activities reach beyond pre-existing netwo...
CRISIS EVENT COMPARISON
● #qldfloods 2011
● #qldfloods 2013
● #eqnz 2011
● #eqnz 2013
TWITTER GROWTH IN AUSTRALIA
FOLLOWER NETWORKS
Perth
Marketing / PR
Design
Web
Creative
Farming
Agriculture
Hardline
Conservatives
Conservatives
Journa...
INFORMATION DISSEMINATION
Dissemination of Julia Gillard’s “misogyny” speech, 9 Oct. 2012
(from Bruns & Sauter, “Anatomie ...
INFORMATION DISSEMINATION
Dissemination of Julia Gillard’s “misogyny” speech, 9 Oct. 2012
(from Bruns & Sauter, “Anatomie ...
OUTLOOK
PRACTICAL CHALLENGES
• Data access, platform volatility
• Research ethics
– textual research using public texts, or ‘human...
PRACTICAL CHALLENGES
• Better integration with existing social and cultural theory
& empirical work
– Mixed methods, espec...
The map is not the territory
(Korzybski)
http://mappingonlinepublics.net/
@snurb_dot_info
@jeanburgess
@dpwoodford
@timhighfield
@tsadkowsky
@socialmediaQUT –
http...
Mapping Online Publics: New Methods for Twitter Research
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Mapping Online Publics: New Methods for Twitter Research

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Presented by Jean Burgess, Axel Bruns, and Darryl Woodford at the Twitter Analytics workshop, University of Göttingen, 12 June 2014.

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  • OffSpring spoilers: http://www.tvtonight.com.au/2013/08/offspring-why-we-killed-off-patrick.html
  • Mapping Online Publics: New Methods for Twitter Research

    1. 1. Mapping Online Publics: New Methods for Twitter Research Axel Bruns, Jean Burgess, and Darryl Woodford ARC Centre of Excellence for Creative Industries and Innovation Queensland University of Technology Brisbane, Australia a.bruns / je.burgess / dp.woodford @ qut.edu.au http://mappingonlinepublics.net/
    2. 2. SOCIAL MEDIA RESEARCH AND ‘BIG DATA’ • Social media as the ‘big data’ moment in HASS research • But ‘big data’ + ‘social media’ almost always = ‘Twitter data’ • Computational social science – e.g. MSR NYC; epidemiology; election & stock market prediction
    3. 3. Scott A. Golder*, Michael W. Macy (2011) Diurnal and Seasonal Mood Vary with Work, Sleep, and Day length Across Diverse Cultures, Science 333 (6051): 1878-1881
    4. 4. SOCIAL MEDIA RESEARCH AND ‘BIG DATA’ • ‘Computational turn’ in new humanities research: shift from computational tools to a new computational paradigm (Berry, 2012). • Eg shift from ‘close’ to ‘distant’ reading (Moretti); ‘software studies’ (eg Fuller, 2008) and ANT approaches to new media platforms; ‘natively’ digital methods to diagnose patterns of social change (Rogers, 2009). • Intersections between data-driven social media research & critical platform studies (Gillespie)
    5. 5. THE MAPPING ONLINE PUBLICS PROJECT • Australian Research Council (ARC) Discovery Project (2010-13) – $410,000 + ARC Centre of Excellence for Creative Industries and Innovation (CCI) – First comprehensive study of Australian social media use – Computer-assisted cultural analysis: tracking, mapping, analysing networked publics – media, communication & cultural studies’ concerns; combining quantitative analytics with qualitative analysis • Mapping public engagement around politics, crisis, culture on Twitter as part of the broader media ecology • Various spin-offs: ARC Linkage, LIEF, Future Fellowship, ATN-DAAD, etc.
    6. 6. TWITTER AND ITS PUBLICS
    7. 7. HASHTAG PUBLICS: #QLDFLOODS (2011) 10 Jan 2011 11 Jan 2011 12 Jan 2011 13 Jan 2011 14 Jan 2011 15 Jan 2011
    8. 8. HASHTAG PUBLICS: #EGYPT @MENTIONS 1-28 Feb. 2011 15 June to 15 Sep. 2011 @mentions between users tweeting predominantly in Latin (blue) vs. Arabic (green) characters
    9. 9. PERSONAL PUBLICS • How do personal publics (cf. Jan Schmidt) around social media accounts form and dissolve? – No clear data available through Twitter API – But: API delivers lists of followers/followees – And: account creation date for such accounts is known  Method for approximating follower accession (cf. Bruns, Woodford, Sadkowsky, First Monday 19.4) – Benefits: • Shows impact of key events on follower growth • Shows following strategies of central account • Can indicate influx of ‘fake’ followers (followback bots, etc.) • Offers comparative tracking across selected population of accounts – Limitations: • Unable to determine un/refollowing
    10. 10. FOLLOWER ACCESSION
    11. 11. @TONYABBOTTMHR’S FAKE FOLLOWERS (11/12 Aug. 2013)
    12. 12. TWITTER AND THE MEDIA ECOLOGY
    13. 13. TWITTER AND TV: TELEMETRICS • The Importance of Hype • Social Media Audiences • From Sabermetric to Telemetrics • The HypometerTM • Work with Katie Prowd and partially funded by QUTBluebox
    14. 14. THE IMPORTANCE OF HYPE • Traditional ratings measure what people have watched, but have limited impact on what people *will* watch. • That is, they are divorced from the “decision moment”. • Yet, companies spend millions promoting shows and attempting to influence viewer behaviour, both through TV ads and through social media. • How do we measure that?
    15. 15. SOCIAL MEDIA RATINGS • Contemporary commercial social media ratings have the same limitation, even if you trust their numbers Cable Channel vs Major Network? Why is having more followers the important statistic? 2.5 hour special 1 hour show
    16. 16. BUT YOU NEED TO GET IT RIGHT Beamly Screenshot: 31 May 2014
    17. 17. BUT YOU NEED TO GET IT RIGHT Beamly Screenshot: 31 May 2014
    18. 18. BIG BROTHER USA VS AU (AUDIENCE) • In US, for Big Brother (& shows generally), there is little correlation between viewers and tweets. • In Australia, for Big Brother and other shows in our pilots, high correlation between viewer count and tweets. • Applying US models to Australia is not possible. • Algorithms need to adjust for their local environment and industry context
    19. 19. SEASONAL MODELS • Blue Line represents the ratio of total viewers, Orange Line represents ratio of tweets (to season average per show). • In both one run seasons (top) and those with mid- season break (bottom), tweets are highly exaggerated version of traditional ratings model. • In other words: Users tweet much more around premieres & finales than regular shows. Metrics must account for this.
    20. 20. THE HYPOMETERTM • iOS app developed as functional prototype to act as a ‘modern TV Guide’ for Australian television • Calculates ‘hype’ via a proprietary algorithm which accounts for national and industry context • Ongoing evaluation of both hype figures and predictions vs. post-show TV ratings and social media engagement. • Clear trend towards ‘dynamic’ audiences; a proportion of the population on whom broadcasters should focus.
    21. 21. SOME EARLY DATA Underpromoted Overperforming High performingUnderperforming
    22. 22. TWITTER AND THE NEWS: ATNIX • Australian Twitter News Index (ATNIX): – Long-term project to track all tweets sharing links to Australian news sites – Tracking, link extraction, processing, analysis • Outcomes include: – Day-to-day (second-by-second) volume of links being shared – Course-of-day / course-of-week activity patterns – Trending stories, long-term issues – Distribution of attention across sites, and change over time – Impact of paywalls and other changes to site structure and functionality – Mindshare? Marketshare? • Further extensions: – Translation to other mediaspheres: DETNIX, NOTNIX, SWETNIX – Comparison with other platforms/practices: Facebook, Hitwise, internal data
    23. 23. TWITTER: THE BIG PICTURE
    24. 24. TWITTER IN AUSTRALIA (AND BEYOND) • Putting Twitter into context: – Do specific activities reach beyond pre-existing networks? – Can we benchmark the phenomena we observe? – How do individual events compare to each other? • Research needs: – Background information on underlying follower/followee network structures in Australia  How (and how far) does information travel, whom does it reach? – Baseline data on everyday average Twitter activity patterns in Australia  How extraordinary are extraordinary events, compared to “normal”? – Standardised metrics for a wide range of events and occurrences  Do similar events unfold similarly? Can we use this to identify them?
    25. 25. CRISIS EVENT COMPARISON ● #qldfloods 2011 ● #qldfloods 2013 ● #eqnz 2011 ● #eqnz 2013
    26. 26. TWITTER GROWTH IN AUSTRALIA
    27. 27. FOLLOWER NETWORKS Perth Marketing / PR Design Web Creative Farming Agriculture Hardline Conservatives Conservatives Journalists ALP Progressives Greens News Opinion News NGOs Social Policy IT Tech Social Media Tech PR Advertising Real Estate Property Jobs HR Business Business Property Parenting Mums Craft Arts Food Wine Beer Adelaide Social ICTs Creative Design Fashion Beauty Utilities Services Net Culture Books Literature Publishing Film Theatre Arts Radio TV Music Dance Hip Hop Triple J Talkback Breakfast TV CelebritiesCycling Union NRL Football Cricket AFL Swimming V8s Evangelicals Teaching e-Learning Schools Christians Hillsong Teens Jonas Bros. Beliebers @KRuddMP @JuliaGillard Follower/followee network: ~120,000 Australian Twitter users (of ~950,000 known accounts by early 2012) colour = outdegree, size = indegree
    28. 28. INFORMATION DISSEMINATION Dissemination of Julia Gillard’s “misogyny” speech, 9 Oct. 2012 (from Bruns & Sauter, “Anatomie eines Trending Topics”, DGPuk Vienna, 8 Nov. 2013)
    29. 29. INFORMATION DISSEMINATION Dissemination of Julia Gillard’s “misogyny” speech, 9 Oct. 2012 (from Bruns & Sauter, “Anatomie eines Trending Topics”, DGPuk Vienna, 8 Nov. 2013)
    30. 30. OUTLOOK
    31. 31. PRACTICAL CHALLENGES • Data access, platform volatility • Research ethics – textual research using public texts, or ‘human subjects’ research using personal data? – Consequences of public/personal convergence, data markets/open data movement, and ‘context collapse’ (boyd) – Cross-national and cross-disciplinary differences, need for public discussion
    32. 32. PRACTICAL CHALLENGES • Better integration with existing social and cultural theory & empirical work – Mixed methods, especially integration of qualitative and ethnographic approaches • Propagation and regularisation of methods (and consequences for research training) – ‘Code literacy’ sufficient to engage with the material consequences of software platforms
    33. 33. The map is not the territory (Korzybski)
    34. 34. http://mappingonlinepublics.net/ @snurb_dot_info @jeanburgess @dpwoodford @timhighfield @tsadkowsky @socialmediaQUT – http://socialmedia.qut.edu.au/
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