lak15 twitter archeology
TRANSCRIPT
TWITTER ARCHEOLOGY OF LEARNING ANALYTICS AND KNOWLEDGE CONFERENCES
Bodong Chen, University of MinnesotaXin (Cindy) Chen, Purdue UniversityWanli Xing, University of Missouri
#LAK15, Marist College, Poughkeepsie, NY, March 20, 2015Authors first met at the LAK14 Doctoral Consortium. Thanks, LAK!
@bodong_c@magic_cindy@helloworld_xing
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Source: http://sciblogs.co.nz/
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Source: http://www.explara.com/magazine/
Twitter as a “Backchannel”
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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?
Why “Twitter Archeology”?
● Not all have published yet● Not all are interested in publishing● Broader participation & richer
interactions (cf. citing)
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Questions
● Did Twitter enable participation and conversation?
● Was participation persistent?● Social dynamics & change over time?● Underlying topics & change over time?
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(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
(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.”
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An Overview of Analyses
● Descriptive Analysis● “Flow” of Twitter Participants● Interaction Social Networks● Evolution of Topics
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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%)
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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)
LAK14 8.6 (30.9) 3.5 (10.4) 1.6 (7.5) 3.5 (13.5) 1.5 (6.0)
Means and standard deviations of activities of individuals
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* 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
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# of years of participation
Interaction Networks based on retweets,
replies and mentions
* The node size and color are based on betweenness centrality.
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
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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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Types of Topics
1. Information-sharing related to conferences and the community
2. Experience-sharing and comments3. Specific research topics (e.g., MOOC,
assessment, students, course design)
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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
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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
Thank You!@bodong_c
[email protected]://meefen.github.io/
Special thanks to Martin Hawksey & all LAK tweeters!
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