Notes towards the Scientific Study of Public Communication on TwitterAssociate Professor Axel BrunsAssociate Professor Jean Burgess@snurb_dot_info | @jeanburgesshttp://mappingonlinepublics.net/Queensland University of Technology
CCI INTERNET AND SOCIETY PROJECTSMEDIA ECOLOGIES & METHODOLOGICAL INNOVATION(Axel Bruns, Jean Burgess, Tim Highfield et al.)Development of new interdisciplinary methods (especiallycomputational methods) for media and communication studies, in orderto better map, track and analyse the changing media environment.MAPPING ONLINE PUBLICS- Large-scale blogosphere mapping and (mostly) Twitter analysis- Crisis, politics, culture – e.g. #qldfloods #ausvotes #royalwedding- Comprehensive project website and blog at http://mappingonlinepublics.net/
‘BIG DATA’ & INTERNET RESEARCH• Big Data as currency across the sciences and social sciences• Business intelligence & ‘data markets’ (including social media & online behavioral data)• ‘Computational turn’ in new humanities research: shift from computational tools to a new computational paradigm, changing the ontologies and epistemologies of humanities research (Berry, 2012)• E.g. shift from ‘close’ to ‘distant’ reading (Moretti); ‘software studies’ (e.g. Fuller, 2008) and ANT approaches to new media platforms• From ‘virtual’/trad social research methods to ‘natively’ digital methods to diagnose patterns of social change (Rogers, 2009)
DATA-DRIVEN SOCIAL MEDIA RESEARCH• Twitter – an emergent and dynamic site of public communication• Wide range of quantitative and qualitative methods in humanities & social sciences to understand cultures of use, power relations etc.• Computer/information science – data-driven, large-scale ‘computational’ approaches (e.g. SNA, diffusion of information, etc.)• Data-driven, ‘natively’ digital methods within humanities-oriented media and communication studies proving to be very productive• Need for shared basic metrics: – For comparative work (across topics & events; across international research teams) – As a baseline for development of mixed-methods research
DEVELOPING TWITTER METRICS• Key data points available through the Twitter API: – text: contents of the tweet itself, in 140 characters or less – to_user_id: numerical ID of the tweet recipient (for @replies) – from_user: screen name of the tweet sender – id: numerical ID of the tweet itself – from_user_id: numerical ID of the tweet sender – iso_language_code: code (e.g. en, de, fr, ...) of the sender’s default language – source: client software used to tweet (e.g. Web, Tweetdeck, ...) – profile_image_url: URL of the tweet sender’s profile picture – geo_type: format of the sender’s geographical coordinates – geo_coordinates_0: first element of the geographical coordinates – geo_coordinates_1: second element of the geographical coordinates – created_at: tweet timestamp in human-readable format – time: tweet timestamp as a numerical Unix timestamp
DEVELOPING TWITTER METRICS• Additional data points from tweets: – original tweets: tweets which are neither @reply nor retweet – retweets: tweets which contain RT @user… (or similar) • unedited retweets: retweets which start with RT @user… • edited retweets: retweets do not start with RT @user… – genuine @replies: tweets which contain @user, but are not retweets – URL sharing: tweets which contain URLs• Potential uses: – metrics per hashtag – metrics per timeframe (day, hour, minute, second, …) – metrics per user (or group of users) – …
TOWARDS A TYPOLOGY OF TWITTER USES• How are hashtags used (during acute events)? – Gatewatching: • Finding and sharing information about breaking news (before the mainstream media do?) • Ad hoc publics: many URLs, many retweets (even unedited) – Audiencing: • Shared experience of major (foreseen) events • Imagined community of fellow participants: few URLs, limited retweeting• What other uses are there? – Continuing discussions (#auspol, #bundesliga, …) – Memes (#ghettohurricanenames, …) – Emotive hashtags (#fail, #win, #headdesk, …) – What about keywords?
BEYOND HASHTAGS• Publics on Twitter: – Micro: @reply and retweet conversations – Meso: hashtag ‘communities’ – Macro: follower/followee networks Multiple overlapping publics / networks• What drives their formation and dissipation?• How do they interact and interweave?• How are they interleaved with the wider media ecology?• Twitter doesn’t contain publics: publics transcend Twitter
UNDERSTANDING AUSTRALIAN TWITTER USE• What is the Australian Twitter userbase? – Large-scale snowballing project – Starting from selected hashtag communities (e.g. #ausvotes, #qldfloods, #masterchef) – Identifying participating users, testing for ‘Australianness’: • Timezone setting, location information, profile information – Retrieving follower/followee information for each account (very slow)• Progress update: – ~950,000 Australian users identified so far, ~21m connections ~2 million Australian users in total?
THE AUSTRALIAN TWITTERSPHERE? Follower/followee network: ~120,000 Australian Twitter users (of ~950,000 known accounts by early 2012) colour = outdegree, size = indegree
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#AUSPOL Follower/followee network: ~120,000 Australian Twitter users (of ~950,000 known accounts by early 2012) colour = #auspol tweets, size = indegree
#AUSVOTES Follower/followee network: ~120,000 Australian Twitter users (of ~950,000 known accounts by early 2012) colour = #ausvotes tweets, size = indegree
#ROYALWEDDING Follower/followee network: ~120,000 Australian Twitter users (of ~950,000 known accounts by early 2012) colour = #royalwedding tweets, size = indeg.
LOOKING AHEAD• Media & communication studies of social media as a ‘special case’ of the big data paradigm: we can be more reflexive• Entangled with the evolving business models & architectures of social media platforms, shifting and variable regulatory structures, as well as public anxieties around the control and use of our social data• Still need to move beyond ‘snapshots’ and single platform studies (cf. Burgess & Green, 2009): accounting for complexity and change
CHALLENGES• Data access, platform volatility – Limitations of API rules and TOS, lack of public archives – Siloed datagathering, difficult to share and compare – ‘data divide’ (boyd & Crawford) caused by uneven $ and skills distribution
CHALLENGES• Propagation and regularisation of methods (and consequences for research training) – ‘Code literacy’ sufficient to engage with the material consequences of software platforms• Better integration with existing social and cultural theory & empirical work – Mixed methods, especially integration of qualitative and ethnographic approaches
CHALLENGES• Research ethics – textual research or ‘human subjects’ research? – Consequences of public/personal convergence, data markets/open data movement, and ‘context collapse’ (boyd) – Cross-national and cross-disciplinary differences, need for public discussion (Burgess & Puschmann, in progress)