twitter pres slides3

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Company Proprietary and Confidential Copyright Info Goes Here Just Like This

TWITTERSPHERE #ausvotesAUGUST 2012


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• 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

WHAT IS TWITTER?

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WHO IS USING IT – THE TWITTERATI?

• 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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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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TRENDING

• 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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ALGORITHMS, SPAMBOTS AND KLOUT

• 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

https://twitter.com/Horse_ebooks

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HEY ‘TRENDING’ SOUNDS A BIT LIKE MARKETING -#ausvotes AND TWITTER AS A SOCIAL RESEARCH TOOLHEY ‘TRENDING’ SOUNDS A BIT LIKE MARKETING -#ausvotes AND TWITTER AS A SOCIAL RESEARCH TOOLHEY ‘TRENDING’ SOUNDS A BIT LIKE MARKETING -#ausvotes AND TWITTER AS A SOCIAL RESEARCH TOOLHEY ‘TRENDING’ SOUNDS A BIT LIKE MARKETING -#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



https://twitter.com/

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BRUNS’ #ausvotes DATASET

• 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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BRUNS’ #ausvotes STATS

• #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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OVERALL…

• 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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LEADERS’ AND JOURNALISTS’ DATA

• 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

• 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




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CONCLUSIONS

• 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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QUESTIONS AND THOUGHTS

• 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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twitter pres slides3

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