Visual network analysis of
Twitter data for co-organizing
conferences: Case CMAD 2013
Jari Jussila1, Jukka Huhtamäki1, Kai...
Overview of the study
• The aim of this research was to explore
what kinds of insights information
visualization of social...
Visual network analysis
•
•

Network analysis introduces a set of methods, practices and metrics for
supporting the invest...
Cumulation of the collected
tweets before, during and after
CMAD 2013

1.2.2014

4
Snapshot of two-mode network of people tweeting and their discussion
topics before, during and after the conference day. I...
Network of people tweeting

1. the most influential people
2. people with similar interests
3. most interesting presentati...
Network of hashtags

1. most discussed topics
- cmadfilabel
- sketchnotes
- streaming
- swarm
2. most interesting
concepts...
Discussion and conclusions
• Insights of network of people
– Help to identify most interesting content for planning future...
DOWNLOAD
http://urn.fi/URN:NBN:fi:tty-201401221053

CITATION
Jussila, Jari; Huhtamäki, Jukka; Henttonen, Kaisa; Kärkkäinen...
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Visual network analysis of Twitter data for co-organizing conferences: Case CMAD2013

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Visual network analysis of Twitter data for co-organizing conferences: Case CMAD2013 presented at HICSS2014 conference. This research is sponsored by Tekes – the Finnish Funding Agency for Technology and Innovation (Projects “Soila”; Innovative Value Creation and Business Models of Social Media in B2B Networks, and “Reino”; Relational Capital for Innovative Growth Companies).

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Visual network analysis of Twitter data for co-organizing conferences: Case CMAD2013

  1. 1. Visual network analysis of Twitter data for co-organizing conferences: Case CMAD 2013 Jari Jussila1, Jukka Huhtamäki1, Kaisa Henttonen2, Hannu Kärkkäinen1, Kaisa Still3 1 Tampere University of Technology 2 Lappeenranta University of Technology 3 VTT Technical Research Centre of Finland
  2. 2. Overview of the study • The aim of this research was to explore what kinds of insights information visualization of social media data can provide for co-organizing conferences • Case study – The CMAD2013 (Community Manager Appreciation Day) event held during 28 January 2013 in Finland – 155 people participated in CMAD2013 event, and 223 people to the live stream during the day – We collected a total of 2686 tweets over a six-week period starting from January 21, 2013. 2138 tweets were exchanged during the day – On the average, one participant sent more than 5 tweets during the day, even if also the online users were counted in 2
  3. 3. Visual network analysis • • Network analysis introduces a set of methods, practices and metrics for supporting the investigation and representation of social media data. We applied the following visual network analysis: 1. A two-mode network including two types of nodes, representing both Twitter users and hashtags. Where a pair of users is connected to each other when one has mentioned the other. Users are also connected to the hashtags they have used in their tweets as well as to the hashtags that are used in the tweets they have been mentioned in 2. One mode network of interconnections between people communicating over Twitter that is users mentioning each other in tweets through commenting, discussions and retweets 3. One mode network of co-occurence of hashtags included in the tweets • We complemented the network analyses with temporal analysis through timeline views. For this, we followed an approach that Ebner and Reinhardt (2009) introduced in which two splines are used to show the cumulation of tweets. Reference: Ebner, M. and Reinhardt, W. Social networking in scientific conferences–Twitter as tool for strengthen a scientific community. Proceedings of the 1st International Workshop1.2.2014 on Science, (2009).3
  4. 4. Cumulation of the collected tweets before, during and after CMAD 2013 1.2.2014 4
  5. 5. Snapshot of two-mode network of people tweeting and their discussion topics before, during and after the conference day. Interactive version is available: http://www.tut.fi/novi/hicss2014/ 1.2.2014 5
  6. 6. Network of people tweeting 1. the most influential people 2. people with similar interests 3. most interesting presentations 1.2.2014 6
  7. 7. Network of hashtags 1. most discussed topics - cmadfilabel - sketchnotes - streaming - swarm 2. most interesting concepts and presentations 1.2.2014 7
  8. 8. Discussion and conclusions • Insights of network of people – Help to identify most interesting content for planning future conferences, e.g. which tracks and topics – Help to identify people with similar interests and for example plan sessions or networking events that interest certain groups of people • Insights of Twitter hashtag networks – Provide implications how the discussion could be better designed and facilitated, for instance, discussions tend to scatter when hashtags are created bottom-up – Also bring forward that Twitter data where the content links were created bottomup, e.g. by the conference participants, in some cases led to broken links or discontinued services and thus missing data. Missing data in the sense that the content of the link is no longer available, and as a consequence does not accumulate to the knowledge base of past and future conferences 8
  9. 9. DOWNLOAD http://urn.fi/URN:NBN:fi:tty-201401221053 CITATION Jussila, Jari; Huhtamäki, Jukka; Henttonen, Kaisa; Kärkkäinen, Hannu; Still, Kaisa 2014. Visual network analysis of Twitter data for coorganizing conferences: case CMAD 2013. Proceedings of the 47th Annual Hawaii International Conference on System Sciences, January 6-9, 2014, Computer Society Press, 2014, 1474-1483. ACKNOWLEDGMENTS This research is sponsored by Tekes – the Finnish Funding Agency for Technology and Innovation (Projects “Soila”; Innovative Value Creation and Business Models of Social Media in B2B Networks, and “Reino”; Relational Capital for Innovative Growth Companies). 1.2.2014 9
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