This is a very interesting way to operationalize the measurement of participation. In my work (asynchronous collaboration by document annotation) I have operationalized participation as total word count (4 or more letters). That is easy for me because I capture the word count in a log file that also contains the participant's identification. I can see the need to develop a normalizing coefficient that would consider the relative prolixity of each participant. Some participants post huge adjectival annotations, others many small and terse messages. Message counts are thus pretty meaningless. The measurement of quality is of course in great need of research. Philip Howard wrote:
One possibility that would not involve writing a program, would be to calculate the distribution of messages in the same way that economists calculate the distribution of wealth in a country. They create an index of population (0% of the people to 100% of the people) and an index of the wealth (0% of the wealth to 100% of the wealth). In a perfectly egalitarian country 10% of the people have 10% of the wealth, 50% of the people have 50% of the wealth, etc, and you can graph the slope 1=1. In an inegalitarian country 10% of the people have 40% of the wealth, 50% of the people have 80% of the wealth and when you graph all of the points you get a curve of inequality. There is a basic calculus method for calculating the area of the graph between the perfectly egalitarian distribution (1=1 slope) and the ineglitarian distribution (wierd slope), and this is called the gini coefficient. Scandinavian countries ahve a small gini coefficient and Brazil has a big gini coefficient. So you might take a sample day or week, count the messages, and then label each message by the author's name. Say you have 1000 messages but only 100 authors. Graph this, 10% of the authors write XX% of the messages, 20% of the authors write XXX% of the messages, and so on up to 100% of the authors writing 100% of the messages. The area of the graph between the egalitarian distribution of messages and your sample gives you a measure of content inequality - how much of the content is generated by an elite group. If the metric is low your group is pretty egalitarian, if the metric is high you have a clique generating most of the content. The metric would be most meaningful if you could compare it to another group's metric, so if you did this over several sample periods, you could say whether the distribution of message posts was getting more or less egalitarian / elitist as the list was evolving.
would make purdy graphs, and could be used for almost any sample of content & authors where you can attribute 100% of the content to 100% of the authors. p. Philip N. Howard Assistant Professor Department of Communication University of Washington http://faculty.washington.edu/pnhoward/
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-- Charlie Hendricksen, PhD Research Collaboration Architect "Information technology structures human relationships." Dissertation link: http://faculty.washington.edu/bkn/public/pubs/diss.html DocReview link: http://purl.oclc.org/DocReview/get