This document summarizes research on analyzing the liveliness of issue terms on Twitter over time using hashtag mining. The researchers focused on: (1) how the frequency and co-occurrence of top hashtags varied across six two-week intervals from February to June; (2) profiling the top connected users mentioning each hashtag and identifying bot accounts; (3) analyzing the URLs shared in tweets containing specific hashtags; and (4) creating associational profiles by visualizing the co-occurrence of hashtags over time. The researchers aimed to detect changing issue associations rather than just what topics rose and fell in frequency.

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

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 active
and changes in association.
• Defining and applying different measures of liveliness (frequency, co-word; per
interval, per day).

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. What we did
1. Variation of key 3. Actor profiling per
hastags over time hashtag over time
4. Associational
2. Mapping users profiles per hashtag

5. 1. Hashtags over time
• Question: What are the top hashtags per interval and how do
they vary over time based on frequency vs. co-word measures?
• Address relation between frequency & co-word measures.
METHOD: Identify top hashtags per interval (A. by
frequency and B. by co-word degree), determine
most frequent hashtags per interval for both as tube.

6. 1. Hashtags over time

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#),!%,#$"
#+,!%,#$"
$#,!%,#$"
$%,!%,#$"
issue transformer)
$',!%,#$"
$),!%,#$"
$+,!%,#$"
Top hashtags per day.
%#,!%,#$"
!$,!&,#$"
!&,!&,#$"
!(,!&,#$"
!*,!&,#$"
• Bursts have short durations.
#!,!&,#$"
#$,!&,#$"
• The day as relevant time unit:
are we too focused on intervals?
• Frequency helps us understand
#&,!&,#$"
#(,!&,#$"
what is a hashtag (publicity device,
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!#,!(,#$"
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!%,!(,#$"
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1. Hashtags over time
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-6180<=01:.<9."

8. 2. Users
Network of top connected users

9. 2. Users
Top 60 users: human/bot proportion
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Top 60 users: bot actor types
!"#$%&# ,-.,/,.012#
$*"#+$&# -345#
167/,58#
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10. 2. Users
Bot activity patterns

11. 2. Users
Human activity pattern

12. 3. Hashtag URL profiling
METHOD: Identify hashtags for URL
profiling, extract URLs.
Hashtags: #ows, #tcot

13. 3. Hashtag URL profiling

14. 3. Hashtag URL profiling

15. 4. Associational profile
Hashtag lifelines by exploring
associational profiles.
Method:
• Select top-connected hashtags for
profiling.
• Create profiles for each hashtags
• Visualise as streamgraph.

16. 4. Associational profile

17. 4. Associational profile

18. 4. Associational profile

19. 4. Associational profile

20. Thank you.
Questions?