Mapping Online Publics: New Methods for Twitter Research

Axel Bruns, Jean Burgess, and Darryl WoodfordARC Centre of Excellence for Creative Industries and InnovationQueensland University of TechnologyBrisbane, Australiaa.bruns / je.burgess / dp.woodford @ qut.edu.auhttp://mappingonlinepublics.net/

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

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

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)

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.

TWITTER AND ITS PUBLICS

HASHTAG PUBLICS: #QLDFLOODS (2011)

10 Jan 201111 Jan 2011 12 Jan 201113 Jan 201114 Jan 201115 Jan 2011

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

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

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 HypometerTM

• Work with Katie Prowd and partially funded by QUTBluebox

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?

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

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 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

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.

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.

SOME EARLY DATA

UnderpromotedOverperforming

High performingUnderperforming

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

TWITTER: THE BIG PICTURE

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?

CRISIS EVENT COMPARISON

● #qldfloods 2011● #qldfloods 2013● #eqnz 2011● #eqnz 2013

TWITTER GROWTH IN AUSTRALIA

FOLLOWER NETWORKS

PerthMarketing / PR

DesignWeb

Creative

FarmingAgriculture

HardlineConservatives

ConservativesJournalists

ALPProgressives

Greens

News

OpinionNews

NGOsSocial Policy

ITTech

Social MediaTechPR

Advertising

Real EstateProperty

JobsHR

Business

BusinessProperty

Parenting

Mums CraftArts

FoodWine

Beer

Adelaide

SocialICTs

CreativeDesign

FashionBeauty

UtilitiesServices

Net Culture

BooksLiteraturePublishing

FilmTheatre

Arts

RadioTV Music

DanceHip Hop

Triple JTalkback

Breakfast TVCelebritiesCycling

Union

NRL

FootballCricket

AFL

SwimmingV8s

Evangelicals

Teachinge-Learning

Schools

ChristiansHillsong

Teens

Jonas Bros.Beliebers

Australian Bands

@KRuddMP@JuliaGillard

Follower/followee network:~120,000 Australian Twitter users(of ~950,000 known accounts by early 2012) colour = outdegree, size = indegree

INFORMATION DISSEMINATION

Dissemination of Julia Gillard’s “misogyny” speech, 9 Oct. 2012(from Bruns & Sauter, “Anatomie eines Trending Topics”, DGPuk Vienna, 8 Nov. 2013)

INFORMATION DISSEMINATION

Dissemination of Julia Gillard’s “misogyny” speech, 9 Oct. 2012(from Bruns & Sauter, “Anatomie eines Trending Topics”, DGPuk Vienna, 8 Nov. 2013)

OUTLOOK

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

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

The map is not the territory (Korzybski)

http://mappingonlinepublics.net/

@snurb_dot_info@jeanburgess@dpwoodford@timhighfield@tsadkowsky

@socialmediaQUT – http://socialmedia.qut.edu.au/