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Visualising Collaboration via Email: Finding the Key Players

Visualising Collaboration via Email: Finding the Key Players. Onn Azraai Puade, Theodor G Wyeld ( IEEE 2006). Advisor ： Dr. Koh Jia-Ling Speaker ： Tai Yi-Ling Date ： 2008.10.30. Outline. Introduction Collaboration Email Visualization Case study Network Diagram Analysis

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Visualising Collaboration via Email: Finding the Key Players

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  1. Visualising Collaboration via Email: Finding the Key Players Onn Azraai Puade, Theodor G Wyeld ( IEEE2006) Advisor：Dr. Koh Jia-Ling Speaker：Tai Yi-Ling Date：2008.10.30

  2. Outline • Introduction • Collaboration Email Visualization • Case study • Network Diagram Analysis • Email Content Analysis • Visualizing Collaboration Impact • Discussion

  3. Introduction • Email is an important form of asynchronous communication. • Email often forms the backbone to research, industry, educational and other collaborations. • Visualizing analyses of email communication patterns during a collaborative activity help us better understand the nature of collaboration, and identify the key players.

  4. Introduction • This paper outlines a proof-of-concept prototype collaborative email visualisation schema. • A new and novel method to identify the key players in a collaboration exercise based on their impact on the group. • It forms its conclusions based on how the individual players rate the importance of each other’s emails.

  5. Case Study • The collaboration involved the organization and running of a workshop to develop resources for a multi-user game. • The workshop ran for three days. 20 individuals from 6 organizations were involved in the activity over 197 days. • The participants of ages 21-51 in this study came from diverse backgrounds.

  6. Case Study • Email as a communication tool was assumed. • The period chosen for analysis is just before and after the workshop was run. • There were 24 emails sent by 10 participants over this period.

  7. Case Study • Each email included embedded prior emails, subject descriptions, sender, receiver(s), date and message. • From this data, we were able to plot the connection between participants and the types of topics discussed.

  8. Network Diagram Analysis • Network graphs were constructed from the collection of emails. • Node and link graphs were generated using Pajek , a social network analysis visualization tool. • Undirected graphs: • vertices -> emails • Nodes -> participants

  9. Network Diagram Analysis

  10. Network Diagram Analysis • adding email nodes

  11. Email Content Analysis • Automatic classification by data mining and information retrieval techniques can be seen in many research studies. • To demonstrate this we recast Divitini and Farshchian’s [8] email roles as a classification system. • According to their content: • A = Awareness • D = Decision making • E = Accessing expert • F = Feedback • R = Resolving issues

  12. Email Content Analysis

  13. Email Content Analysis • Conducting a survey with the participants identified in the 24 email collection. • To rate each individual email in term of its importance on a scale of: • 0 – Not applicable • 1 – Not important • 2 – Important • 3 – Very important

  14. Email Content Analysis • Re-organize the table by number of emails per participant, type, ratings and average ratings

  15. Email Content Analysis • Average rating represents the ‘loudness’ (L) of aparticipant’s message. • And multiply their loudness by the number of emails (N) sent, this is a measure of their overall ‘impact’ (I) on the collaboration. • L X N = I

  16. Email Content Analysis

  17. Email Content Analysis

  18. Visualizing Collaboration Impact • Then visualize the results of these tabulations. • Both these visualisations help us to gain ‘at a glance’ a better understanding of the information contained in the tables. • It includes: • Orange dot – number of mail • Blue dot – loudness • Dashed circle – impact • Concentric ring – four rating scales

  19. Visualizing Collaboration Impact

  20. Visualizing Collaboration Impact

  21. Discussion • From the visualisation of these twoanalyses, there was more variation between participants in both loudness and impact. • There was little variation in loudness between the different types of email, But there was greater variation between impact than that displayed in the participant impact.

  22. Discussion • Predefined roles, such as project leader, manager, coordinator, and so on, do not necessarily generate the greatest impact on a collaborative project over time. • The traditional purpose of these roles – to make announcements on progress, meetings, and queries – is supported by the ‘by-type’ visualisation.

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