This document summarizes a study analyzing Twitter data from several Learning Analytics and Knowledge (LAK) conferences between 2011-2015. The study found that over time, there was an extended reach of Twitter participation at the conferences, denser and more reciprocal interaction networks formed, and a wider diversity of research topics were discussed. However, there was also peripheral and inconsistent participation. The study provided an overview of descriptive analyses of the Twitter data, models of the "flow" of participants between conferences and interaction networks, and analyses of hashtags and topics discussed over time. Limitations and opportunities for future related work were also discussed.

1. TWITTER ARCHEOLOGY OF
LEARNING ANALYTICS AND
KNOWLEDGE CONFERENCES
Bodong Chen, University of Minnesota
Xin (Cindy) Chen, Purdue University
Wanli Xing, University of Missouri
#LAK15, Marist College, Poughkeepsie, NY, March 20, 2015
Authors first met at the LAK14 Doctoral Consortium. Thanks, LAK!
@bodong_c
@magic_cindy
@helloworld_xing
#LAK15Meta

2. Source: http://eeecos.org/
Why Twitter at Conferences?

3. Source: http://sciblogs.co.nz/

4. Source:http://stillwaterhistorians.com/

5. Source: http://www.explara.com/magazine/
Twitter as a “Backchannel”

6. #LAK15Meta
1. When did you first tweet about #LAK?
2. Why/What do you tweet(e.g., comments, info, beer)?
3. Who did you get to know through #LAK?
4. What is your primary research topic?

7. Why “Twitter Archeology”?
● Not all have published yet
● Not all are interested in publishing
● Broader participation & richer
interactions (cf. citing)
#LAK15Meta

8. #LAK15Meta

9. #LAK15Meta

10. Questions
● Did Twitter enable participation and
conversation?
● Was participation persistent?
● Social dynamics & change over time?
● Underlying topics & change over time?
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11. (Cleaned) Dataset
Conference Participants Tweets
LAK11 215 1358
LAK12 606 4050
LAK13 280 2223
LAK14 362 3105
* Data wrangling challenges: inconsistencies of data shapes across years; a
systematic mistake of user ids in the 2011 archive; parsing interactions; etc.
3587 (by last night)LAK15 465

12. (Cleaned) Dataset
Conference Participants Tweets
LAK11 215 1358
LAK12 * 606 4050
LAK13 280 2223
LAK14 362 3105
* LAK12: “A substantial amount of tweets during LAK12 was about
the technologies adopted for live video streaming.”
#LAK15Meta

13. An Overview of Analyses
● Descriptive Analysis
● “Flow” of Twitter Participants
● Interaction Social Networks
● Evolution of Topics
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14. Descriptive: Conferences
Conference Tweets Retweets Replies
LAK11 1358 450 (33.1%) 230 (16.9%)
LAK12 4050 1207 (29.8%) 430 (10.6%)
LAK13 2223 570 (25.6%) 363 (16.3%)
LAK14 3105 1255 (40.4%) 570 (18.4%)
#LAK15Meta

15. Descriptive: Individuals
Outgoing Incoming
Conf Tweets Retweets Replies Retweets Replies
LAK11 6.3 (14.1) 2.1 (4.7) 1.1 (3.5) 2.0 (7.5) 0.9 (3.4)
LAK12 6.7 (23.8) 2.0 (5.0) 0.7 (2.9) 1.9 (13.4) 0.6 (3.7)
LAK13 7.9 (31.5) 2.0 (4.5) 1.3 (7.4) 2.0 (7.7) 1.1 (4.2)
Means and standard deviations of activities of individuals
#LAK15Meta

16. “Flow” of Participants
#LAK15Meta

17. * The only reason you see a pie here is we just celebrated a big Pie Day – 3.14.15 ;)
1,217 unique
participants
Peripheral
participation
#LAK15Meta
# of years of participation

18. Interaction Networks
based on retweets,
replies and mentions
* The node size and
color are based on
betweenness centrality.

19. Interaction Networks
Conf Nodes Edges Avg
Degree
Avg Path
Length
Reciprocate
Rate
# of
Communities
LAK11 215 569 2.65 2.95 .13 3
LAK12 606 1521 2.51 3.1 .13 6
LAK13 280 736 2.63 2.99 .15 5
LAK14 362 1369 3.78 2.73 .20 6
#LAK15Meta

20. Content: Hashtags

21. Content: Topics
● Latent Dirichlet Allocation (LDA)
○ R package: topicmodels
○ Optimal # of topics: 34
● Make sense of topics
○ R package: LDAvis
○ Interactive exploration, clustering
● Track selected topics
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22. Select a cluster

23. Select a topic

24. Select a term

25. Types of Topics
1. Information-sharing related to
conferences and the community
2. Experience-sharing and comments
3. Specific research topics (e.g., MOOC,
assessment, students, course design)
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26. Change with Topics

27. Summary
● An extended reach and increasing interactions
● Denser, more reciprocal networks
● Peripheral and in-persistent participation
● Emergence of multiple sub-communities
● Diverse & fluctuating research topics
#LAK15Meta

28. Limitations & Future Work
● Representativeness of the LAK community
● Potential loss of (earlier) data
● Challenges posed by briefness of a tweet
● Combine tweets and academic publications
● Connect/compare tweeters with authors/attendees
● Compare with other closely related communities (e.
g., EDM, LS)
● Dive into chains of conversation
Collaborative #LAK15Meta

30. Thank You!
@bodong_c
chenbd@umn.edu
http://meefen.github.io/
Special thanks to Martin Hawksey & all LAK tweeters!
#LAK15Meta

31. #LAK15Meta