The document provides an overview of Twitter, including what it is, how it works, and how it is used. It discusses key Twitter concepts like handles, tweets, hashtags, followers and trends. It also summarizes statistics about the #ausvotes hashtag and how it was used during the 2010 Australian election, finding topics like the NBN received more attention than major political figures. Overall, the document examines Twitter as a tool for social research and data analysis.

1. 1
TWITTERSPHERE #ausvotes
AUGUST 2012
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2. WHAT IS TWITTER? 2
• Twitter is a microblogging technology
• Handle: is your Twittername beginning with the @symbol
• Tweets: are statements of 140 characters shared in the Twittersphere
• Followers: are people on Twitter who subscribe to receive your tweets
• Following: are the people whose tweets you have subscribed to receive on
your feed
• Hashtags (#): are a key word added to a tweet which allows a community to
form around a topic for information and discussion
• Retweet is one of your tweets that a follower has forwarded to their followers
• @replies: are direct responses to your tweet, using your handle like an
address
• Direct Messages: are hidden emails you can send to your followers
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3. 3
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4. 4
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5. WHO IS USING IT – THE TWITTERATI? 5
• Celebrities
• Public personalities such as politicians, activists, journalists, academics, public
intellectuals
• Citizens
• Businesses, NGO’s and organisations (Amnesty etc)
• Joke/amusing (Kim Kierkegaardashian – Kierkergaard mashed with KK)
• Spam/adult
• Twitter can adapt to different needs for different personalities
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6. 6
THE SIX ELEMENTS OF HUMAN BEHAVIOUR THAT DRVE SOCIAL MEDIA
(http://www.businessesgrow.com/2012/02/21/the-six-elements-of-human-behavior-that-
drive-social-media/)
• Altruism
• Hedonism
• Homophily
• Memetics
• Narcissism
• Tribalism
• Can we think of any more? FOMO, Crowdpleasing/approval
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7. TRENDING 7
• Twitter monitors the use of hashtags and if they become popular enough, they
become a ‘trend’
• Twitter lists topics that are trending on the left of the screen
• Users can click on a trend and see the conversation that is taking place around
it
• Users can use trends to find like-minded users to follow
TRENDS
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8. ALGORITHMS, SPAMBOTS AND KLOUT 8
• Trends and suggested followees are determined by algorithms
• If a hashtag is mentioned by enough people to become popular, it is ‘trending’
• Trending is personalised according to your interests. This
was changed to make trending more relevant due to
excessive Bieber
• Why do random people follow me on Twitter?
• Visit:
https://twitter.com/Horse_ebooks
• Klout 3rd party app: ‘social influence score’ (uses other platforms too)
• Problem with Twitter: ‘too much noise’. Users have followed so many
accounts that they can no longer understand their own feeds
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9. HEY ‘TRENDING’ SOUNDS A BIT LIKE MARKETING - 9
#ausvotes AND TWITTER AS A SOCIAL RESEARCH TOOL
• Visit: Twitter followers for sale on ebay
http://www.ebay.com.au/sch/i.html?_from=R40&_trksid=p3984.m570.l1313&
_nkw=twitter+followers&_sacat=See-All-Categories
• Can they be used effectively?
• #ausvotes hashtag is the community for Australian political discussion.
• Visit: #ausvotes
https://twitter.com/#!/search/?q=%23ausvotes&src=hash
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10. BRUNS’ #ausvotes DATASET 10
• Article is example of gleaning information from a dataset
• Apps for gleaning data from Twitter include Twapperkeeper, Gawk and
Wordstat
• Fields analysed in the 2010 #ausvotes dataset were mentions, retweets,
@replies and connectivity
• Problems with the dataset: inability to capture follow-on discussion and
retweet fakery.
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11. BRUNS’ #ausvotes STATS 11
• #ausvotes tweets spike around big election events: election day, the debate,
some policy announcements
• An average of 22% of tweets contained URL’s
• 33% were retweets and 20% were @replies – ie. over 50% were responses
• Tweets around the big events were directed to the public discussion
• During breaking news such as a flood, @replying is less statistically prevalent
• Abbott is mentioned more than Gillard, Gillard had most traffic
• Twitter users particularly interested in NBN – stats reflect Twitter demographic
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12. OVERALL… 12
• Stats may be of interest to political pollsters though it seems Twitter discussion
is not driven by the direction of politicians
• #ausvotes was engaged with itself as a public
• Bruns concluded from the above information that #ausvotes was “subcultural
and fannish”, ie. subversive and parodic
• Users of #ausvotes are ‘hardcore’ or ‘diehard’. Also internet and Twitter users,
hashtag users.
• Formerly unconnected people meeting over hashtags? Possibly not, but at
least the group might encourage new voices
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13. LEADERS’ AND JOURNALISTS’ DATA 13
• The two nodes in the network which received the most amount of traffic
(replies and retweets) were Latika Bourke and Annabel Crabb
• These two journalists had the highest betweenness centrality, which is a type
of data representation or data visualisation that is becoming very popular on
the web.
• Betweenness centrality is based on the frequency with which a node is the
shortest point between two nodes in a dataset
• In this case continued use of an ‘information conduit’ would indicate some
faith in it
• This kind of data visualisation is forming the heart of what they are calling data
journalism
• Watch
http://www.youtube.com/watch?v=YaGqOPxHFkc&feature=related
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14. CONCLUSIONS 14
• The article displays the potential for Twitter for infometrics.
• More conclusions will be able to be reached as the data becomes older
• The main potential for Twitter is enabling non-geographic communities and
live news gathering which have discursive aspects rather than a top-down
generation
• A study from a German election concluded that statistical dominance of
mentions of a leaders name was enough to predict outcome – not so in this
case
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15. QUESTIONS AND THOUGHTS 15
• Do the statistics and infographics tell us much about people in general, people
who use Twitter, or not much about people at all?
• What does the fuss around ‘trending’ really mean? Is popularity necessarily
admirable or desirable? What does it obscure?
• Is the organisation of progressive movements the real indicator of Twitter’s
democratic potential, or is the information it generates just more ‘tyranny of
the majority’?
• Need to consider infometrics in terms of epistemology – stay alert
• Discussion question: remember the 6 reasons for using social media I showed
before in relation to data journalism. What do you think we need to keep in
mind about the relationship between complex motivations and data?
THANK YOU
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