Hashtag lifelines

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A digital methods summer school 2012 project

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Hashtag lifelines

  1. 1. Hashtag lifelines A digital methods summer school 2012 project Jill, Colleen, Diego, Johannes, Sara, Albrecht, Allesandro, Kalina, Tally, Esther, Noortje & Carolin
  2. 2. Liveliness of issue terms Focus on hashtag mining as technique for analysing variability of issue terms over time.1. Are hashtags a suitable format for analysing liveliness of issue terms?2. Does co-word provide a useful alternative to frequency measures here?• Rather than defining what rises and falls (Downs 1974), detect what is activeand changes in association.• Defining and applying different measures of liveliness (frequency, co-word; perinterval, per day).
  3. 3. The Dataset• Twitter data for “Climate Change”.• Period: 01.02. - 15.06• Interval: six 2 week intervals• Total 204795 tweets.• Focus on hashtags.
  4. 4. What we did1. Variation of key 3. Actor profiling perhastags over time hashtag over time 4. Associational2. Mapping users profiles per hashtag
  5. 5. 1. Hashtags over time• Question: What are the top hashtags per interval and how dothey vary over time based on frequency vs. co-word measures?• Address relation between frequency & co-word measures.METHOD: Identify top hashtags per interval (A. byfrequency and B. by co-word degree), determinemost frequent hashtags per interval for both as tube.
  6. 6. 1. Hashtags over time
  7. 7. !" #!!" $!!" %!!" &!!" !!" (!!" )!!" *!!" +!!" #!!!"!#,!%,#$"!%,!%,#$"!,!%,#$"!),!%,#$"!+,!%,#$"##,!%,#$"#%,!%,#$"#,!%,#$"#),!%,#$"#+,!%,#$"$#,!%,#$"$%,!%,#$" issue transformer)$,!%,#$"$),!%,#$"$+,!%,#$" Top hashtags per day.%#,!%,#$"!$,!&,#$"!&,!&,#$"!(,!&,#$"!*,!&,#$" • Bursts have short durations.#!,!&,#$"#$,!&,#$" • The day as relevant time unit: are we too focused on intervals? • Frequency helps us understand#&,!&,#$"#(,!&,#$" what is a hashtag (publicity device,#*,!&,#$"$!,!&,#$"$$,!&,#$"$&,!&,#$"$(,!&,#$"$*,!&,#$"%!,!&,#$"!$,!,#$"!&,!,#$"!(,!,#$"!*,!,#$"#!,!,#$"#$,!,#$"#&,!,#$"#(,!,#$"#*,!,#$"$!,!,#$"$$,!,#$"$&,!,#$"$(,!,#$"$*,!,#$"%!,!,#$"!#,!(,#$" !"#$"%#&()*+,-./012&%.3)4.-56%7/.#/89-56%7/.#53/80#:!%,!(,#$"!,!(,#$"!),!(,#$"!+,!(,#$"##,!(,#$"#%,!(,#$"#,!(,#$"#),!(,#$"#+,!(,#$"$#,!(,#$" -7$" -BCDB" -HIEE@" ->?@A?" -14673/" -@EFGEAD@" 1. Hashtags over time -./012./345" -HJDG?JF?IKL@H" -0289:32;02<" -6180<=01:.<9."
  8. 8. 2. UsersNetwork of top connected users
  9. 9. 2. UsersTop 60 users: human/bot proportion *+,-.% /&% 0&)% !"#$% *+,-.% !"#$% &&% ()%Top 60 users: bot actor types !"#$%&# ,-.,/,.012# $*"#+$&# -345# 167/,58# $$"#$&# -345## 9-1-6,12# ,-.,/,.012# !"#$%&# %"#$(&# 8,56# "#)&#"#)&#
  10. 10. 2. UsersBot activity patterns
  11. 11. 2. UsersHuman activity pattern
  12. 12. 3. Hashtag URL profilingMETHOD: Identify hashtags for URLprofiling, extract URLs.Hashtags: #ows, #tcot
  13. 13. 3. Hashtag URL profiling
  14. 14. 3. Hashtag URL profiling
  15. 15. 4. Associational profileHashtag lifelines by exploringassociational profiles.Method:• Select top-connected hashtags forprofiling.• Create profiles for each hashtags• Visualise as streamgraph.
  16. 16. 4. Associational profile
  17. 17. 4. Associational profile
  18. 18. 4. Associational profile
  19. 19. 4. Associational profile
  20. 20. Thank you.Questions?

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