placodermi

By: Chris Malek

Oct 01 2007

tags: email, visualization

Category: Summaries

Summary of:

Puade, O. A. and T. G. Wyeld (2006). Visualising collaboration via email: Finding the key players. In Information Visualization, 2006. IV 2006. Tenth International Conference on, pp. 124-129.

In this conference paper presented at the 2006 Information Visualization Conference, the authors present a proof-of-concept visualization tool which attempts to depict the relative importance of e-mails and participants in a collaboration with the goal of finding key players. Their goal for the visualization is that they hope that the groups can use it for self-reflection: to learn from what the visualization shows them, and to change to then collaborate better. 24 e-mails from a group of game developers working on a software development project provided the data for the visualization. The authors recruited each of these ten people for the purposes of this study.

Data were prepared for the visualization as follows:

1. First, categorize e-mails, using Divitini and Farshchian’s [2] e-mail roles: “Awareness”, “Decision making”, “Accessing expert”, “Feedback”, “Resolving issues.” A single e-mail can be in more than one of those roles.
2. Ask each of the ten people to rate each of the 24 e-mails in terms of importance on a scale of 0-3, where 0 = not applicable, 1 = not important, 2 = important and 3 = very important.
3. Call the average rating for across messages for each person the “loudness” of that person, and make the “impact” of a person be the “loudness” times the number of e-mails.

They used two visualizations based on cocentric circles divided radially into pie sections. The first visualization impact by participant, and the second by message type (which included, in this case, subtypes). Each figure plotted in the visualization combined number of e-mails, loudness, and impact. They plotted on the same visualization (I think) an undirected graph of e-mails (?).

Analysis

The visualization showed something that they didn’t expect: “there is more variation between how the importance of participant’s emails is perceived compared to the importance of the types of email sent. However, emails of type ‘awareness’ clearly have a greater impact on the collaboration because of their dominant use” (p. 6).

They used Divitini and Farshchian’s [2] work to create an expert system to analyze and categorize this archive, and made some kind of comparison that I have to go back and understand better.

Issues

They mention that “the collaboration involved various activities in different location and continents” (p. 2). I couldn’t tell whether the group was typically co-located or was virtual. They do mention virtual teams on p. 1, in section 1.2.

They mention two other studies which will be very useful to me: Perer et. al. [2] and Divitini and Farshchian [1].

Critique

• Although the authors say that their goal for the visualization is that they hope that the groups can use it for self-reflection, as far as I can tell from the paper, the subjects never see the visualization.
• They analyzed only 24 e-mails — it seems like this is far too small a sample size to truly illuminate social structure.
• Where did this visualization come from? Why choose something like this? No explanation is given.
• Self-evaluation bias? People are rating their own e-mails.
• The introduction of the paper is great — I should use something like it for my own paper.
• The visualization will not scale well to many more people — for small groups only.
• Do all participants have the same definition of “importance”? Does this matter?

Argument

It’s not easy for me to see where they’re going with this work. The argument is not very strong:

They want to help collaboration participants create better collaboration outcomes via self-reflection (p. 2).

1. “Visual representations aid and enable the user to understand the different kinds and forms of data” (p. 1) especially of large amounts of data.
2. Creating an overview allows the user to see the big picture (p. 1).
3. We can create an overview visualization of the structural and behavioral aspects of a collaboration (p. 1)
4. We can use e-mail data to create the overview, because e-mail is a key collaboration medium (p. 1).
5. We can use this overview to identify various user roles and their grand purpose as to the collaboration as a whole (p. 1).
6. Node and edge graphs are misleading in identifying key players (p. 3), so something better is needed.
7. If we classify e-mails by type, importance and impact, we can better identify key roles (pp. 3-4). Do it by hand now; we’ll eventually do it by computer (p. 3). Importance and impact are defined as above, and meaningfully map to social roles.
8. Make a visualization of this data.
9. This visualization will enable collaboration participants to identify key players, and thus to do self-reflection.

The argument gets weaker and weaker after step 5.

References

1. Divitini, M. and B. A. Farshchian (1999). Collaboration and coordination through basic internet tools: A case study. In World MultiConference SCI/ISA, Orlando, Florida.

2. Perer, A., B. Shneiderman, and D. W. Oard (2006, December). Using rhythms of relationships to understand e-mail archives. J. Am. Soc. Inf. Sci. Technol. 57 (14), 1936-1948.