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Classifying Twitter Content
 

Classifying Twitter Content

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Classifying Twitter Content Classifying Twitter Content Presentation Transcript

  • Classifying Twitter Content Dr Stephen Dann Australian National University @stephendann Presented at Marketing Science, Houston, June 11, 2011
  • If you’re on Twitter Questions can be sent to @stephendann or Hashtag #mktsci2011
  • Why here, why now?
    • Why this presentation?
      • MSI interest in role of social media in branding
      • Attitudinal metrics from web can predict transactions
    • Why this method?
      • Try to further avoid the criteriaflation issue
        • Hence announcing a coding structure exists
    • What outcome?
      • I could use a good set of equations
  • Series of Projects
    • Blog Post reacting to Pear Analytics 2009
    • First Monday Paper (Dann, 2010)
    • Marketing Science, Method <-You are here.
    • USF Social Marketing, Social Media in Social Marketing (next week)
    • AMSRS Conference, Crisis Communication Analysis (September)
    • ANZMAC, Categories in Detail (December)
  • Twitter.
    • Twitter matters because of what it is: at its heart, a platform that offers an exchange of ideas and information on an unprecedented scale.
    • Why Twitter Matters : Marketing : Idea Hub :: American Express OPEN Forum http://www.openforum.com/idea-hub/topics/marketing/article/why-twitter-matters-ann-handley Fri Oct 02 2009 21:16:49 GMT+1000 (AUS Eastern Standard Time)
    Twitter in Plain English
  • How to analyze a living medium? Hawthorn Effect*Uncertainty Principle Sample Size / Twitter Volume [ ]
  • Why do any coding?
    • Twitter is not about the aggregate firehose
      • There are those who disagree, and I have cited many of them. However, few, if any actually read the impossibly fast updating full timeline
    • Twitter is about how you use it.
      • Twitter becomes something in co-creation
      • Twitter timeline as documented history
      • Tracking Near-Past Behaviour
  • Raw Counts Tweetstats – www.tweetstats.com
  • Text Analysis Tweetstats – www.tweetstats.com Wordle – wordle.com
  • Prior Analysis
    • Boyd et al 2010
    • Crawford 2009
    • DiMicco, et al 2008
    • Fahmi 2009
    • Gay et al 2009
    • Heany and McClurg 2009
    • Hohl 2009
    • Honeycutt and Herring 2009
    • Jansen et al 2009
    • Java et al 2007
    • Lariscy et al 2009
    • Makice, 2009
    • Miller, 2008
    • Naaman et al 2010
    • Pear Analytics 2009
    • Steiner 2009
    • Zhao and Rosson 2009
    Dann (2010) based on:
  • Schema
    • Developed from ground theory approach
      • 60+ Twitter articles
        • Use behaviours, content analysis, sentiment analysis
      • 10,000+ tweets
        • Manual coding
    • Supporting analysis
      • Linguistic Analysis (LIEC)
        • Automated analysis
      • Leximancer Analysis
  • Framework
    • Six categories.
    • 1. Conversational
    • 2 . News Events
    • 3 . Pass along
    • 4 . Phatic
    • 5 . Status
    • 6 . Spam
  • Conversational
    • core of the interpersonal exchange on Twitter, and the binding activity that links different users together into a sense of community, companionship and conversation
      • Cahill 2009, Cranefield and Yoong 2009, Honeycutt and Herring 2009, Java et al 2009, Perlmutter 2009, Steiner 2009, Ratkiewicz 2010).
    • four identifiable sub components
      • action, query, referral and response
  • News Events
    • broad selection of media releases, citizen journalism, professional journalism, PR and publicity
      • Mäkinen and Wangu Kuira 2008, Power and Forte 2008, Java et al 2009, Phelan et al 2009, Chu et al 2010, Petrovic et al 2010, Zhou et al 2010, Phuvipadawat and Murata 2011, Cheong and Lee 2011).
      • Seven categories:
        • announcements, hashtagged events, headlines, sport, natural disasters, transport and weather.
  • Pass along
    • where Twitter is used as a short form publishing outlet for recommended links, other Twitter remarks, or links to the author’s own content
      • Java et al 2007, Mischaud 2007, Heany and McClurg 2009, Java et al 2009, Pear Analytics, 2009, Naaman et al 2010, Zhang et al 2010, Bakshy et al 2011).
    • Five categories
      • automated endorsement, endorsements, retweet, secondary social media and user generated content,
  • Phatic
    • Use of Twitter as a meanings to maintain a presence within a community, and connections to other users of the service without direct conversation
      • Java et al 2007, Miller, 2008, Henneburg et al 2009, Keenan and Shiri 2009, Makice 2009, Pear Analytics, 2009, Fernando 2010, Marwick and boyd 2010, Zhang et al 2010
    • Four categories
      • undirected broadcast statements, fourth wall breaking meta commentary, greetings and the unclassifiable content
  • Status
    • Use of the service to answer the original Twitter question of “What are you doing?” in terms of reporting the user’s sense of “Me-Now”, or statements of immediately transpired activity
      • Gaonkar et al 2008, Bollen et al 2009, Java et al 2009, Chu et al 2010, Dodds et al 2011, Naaman et al 2010, Zhang et al 2010
    • eight categories
      • activity, automated status, location, mechanical, personal statements, physical, temporal and work
  • Sub categories
    • Conversational
      • Response
      • Referral
      • Query
      • Action
    • News Events
    • Pass along
    • Phatic
    • Status
  • Sub categories
    • Conversational
    • News Events
      • Headlines
      • Hashtagged Event
      • Natural disasters
      • Transport
      • Weather
      • Sport
      • Announcement
    • Pass along
    • Phatic
  • Sub categories
    • Conversational
    • News Events
    • Pass along
      • Retweet
      • Endorsement
      • Secondary Social Media
      • User generated content
      • Automated Endorsement
    • Phatic
    • Status
  • Marketing Science Style
    • N = 11672
      • Three public sector organisation timelines
        • Local government, police force, energy company
      • Two hashtags
        • natural disaster
        • conference
      • One personal timeline data set
  • 1072 1823 4344 1020 602 2811 11672   Total 31 34 126 153 10 834 1188 10% Status 20 24 69 60 12 213 398 3% Phatic 896 949 2780 351 533 278 5787 50% Pass Along 10 31 784 29 17 13 884 8% News Events 115 785 585 427 30 1473 3415 29% Convers-ational Ener. Counc. Police #Conf #Dis. Dann n Data  
  • Uses of the Data
  • Here’s where you come in…
  • The Challenge Time Day Month Year * Spam gets a category indicated as “Delete” 140 characters of text [C] [S] [PA] [N] [P] [X]* [C 1 ] [C 2 ] [C 3 ] [C 4 ] [S 1 ] [S 2 ] [S 3 ] [S 4 ] [S 5 ] [S 6 ] [S 7 ] [S 7 ] [PA 1 ] [PA 2 ] [PA 3 ] [PA 4 ] [PA 5 ] [N 1 ] [N 2 ] [N 3 ] [N 4 ] [N 5 ] [N 6 ] [N 7 ] [P 1 ] [P 2 ] [P 3 ] [P 4 ] [X 1 ] [X 2 ] [X 3 ] [X 4 ]
  • Future plans
    • Segments and Use-Case Scenarios
    • Forward facing strategic guidelines
    • Predictive Models
    • Certain level of automation
    • But not autonomous coding.
  • References
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  • Questions [email_address] Or @stephendann
    • This work is licensed under the Creative Commons Attribution-Share Alike 2.5 Australia License. To view a copy of this license, visit http://creativecommons.org/licenses/by-sa/2.5/au/