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Co-word lifeline
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Co-word lifeline Presentation Transcript

  • 1. Co-word Lifelines Noortje, Esther, Marieke, David & Carolin
  • 2. Identifying key life signals Question: How lively is “climate action” on Twitter. Objective: Use the co-word machine as Issue Biographer. Changing co-words per intervalFocus on key hashtags Focus on intervals (Associational Profile)
  • 3. Temporality & interval• Consideration: compare liveliness measures tofrequency of activity.• Intervals: problems.
  • 4. Dataset• Twitter data “Climate Action”.• Co-word machine input: Tweets.• Focus on three intervals: 15Feb-14Mar, 15Mar-14Apr,15Apr-14May.• Objective: profile the co-word relations of keyhashtags.
  • 5. Machine settings for the lifeline tracker • Normalised association strength. • Flattening co-word relations.
  • 6. Machine settings for the lifeline tracker • Keyword profiling. • Determines changes in associational profile per interval. • Identifies degree of connectivity & change. • New words connecting, words disappearing & current connections.
  • 7. Hashtag co-word network interval I
  • 8. Hashtag co-word network interval II
  • 9. Hashtag co-word network interval III
  • 10. Hashtag profiling over time method 1. Detect key hasthags (av. weighted degree) per interval. 2. Determine URL profile for key hashtags per interval. 3. Determine co-word profile per hashtag per interval. 4. Determine overall variation per hashtags/for all hashtags.
  • 11. Hashtag profiling•#tarsands •#jobs•#eu •#san•#cdnpoli •#intern•#agw •#job•#green •#cop18•#fqd •#cop17•#cndpoli •#climatechange•#politics •#energy•#unfccc •#globalwarming•#ceta •#environment•#health •#policy•#flooding •#losangeles •#nonprofit
  • 12. Hashtag profiling #healthTop weighted degree hashtag #health: Only retweets
  • 13. Hashtag actor profiling #green Interval I Interval II Interval III
  • 14. Co-word machine as Issue BiographerCo-word machine input: Tweets, URLs, medium specific small text units,syntactic demarcation.Settings1. Determine temporal unit: interval or continuous.2. Identification of key words.- manually & measures (degree...).- across set or by interval3. Static vs. dynamic terms (signal words?)4. Determine associational profile per key word (int. or continuous?) by URLand keyword.
  • 15. Keyword lifelineContinuous changes in associational profile as indicatorfor keyword liveliness.
  • 16. Thank you.