Hashtag lifelines

Hashtag lifelines A digital methods summer school 2012 project Jill, Colleen, Diego, Johannes, Sara, Albrecht, Allesandro, Kalina, Tally, Esther, Noortje & Carolin

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).

The Dataset• Twitter data for “Climate Change”.• Period: 01.02. - 15.06• Interval: six 2 week intervals• Total 204795 tweets.• Focus on hashtags.

What we did1. Variation of key 3. Actor profiling perhastags over time hashtag over time 4. Associational2. Mapping users profiles per hashtag

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.

1. Hashtags over time

!" #!!" $!!" %!!" &!!" !!" (!!" )!!" *!!" +!!" #!!!"!#,!%,#$"!%,!%,#$"!,!%,#$"!),!%,#$"!+,!%,#$"##,!%,#$"#%,!%,#$"#,!%,#$"#),!%,#$"#+,!%,#$"$#,!%,#$"$%,!%,#$" issue transformer)$,!%,#$"$),!%,#$"$+,!%,#$" Top hashtags per day.%#,!%,#$"!$,!&,#$"!&,!&,#$"!(,!&,#$"!*,!&,#$" • Bursts have short durations.#!,!&,#$"#$,!&,#$" • 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."

2. UsersNetwork of top connected users

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# "#)&#"#)&#

2. UsersBot activity patterns

2. UsersHuman activity pattern

3. Hashtag URL profilingMETHOD: Identify hashtags for URLprofiling, extract URLs.Hashtags: #ows, #tcot

3. Hashtag URL profiling

4. Associational profileHashtag lifelines by exploringassociational profiles.Method:• Select top-connected hashtags forprofiling.• Create profiles for each hashtags• Visualise as streamgraph.

4. Associational profile

Thank you.Questions?

Hashtag lifelines

by Digital Methods Initiative on Jul 02, 2012

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