In the ethnography, “Situated Learning” (Lave & Wenger, 1991) it was observed that learning a trade or profession such as a tailor or midwifery was best supported by engaging in this activity within the actual community in which it was taking place. In this context the learner, as an apprentice, can be exposed to others with varied skill levels within that particular job or trade from which they can learn. Initially they may engage in some limited tasks such as maintaining inventories of equipment or tools and ordering supplies. Over time and with more exposure to the task their role will evolve and increase in responsibility. For this to take place they must learn from others with more experience. Some members of this particular community may have expert status whereas others may be at more of an intermediary level. At the beginning those new to the community participate only on a peripheral level. As novices they have yet to learn the terms, concepts and practices that would allow them to engage in the profession in a meaningful way. For example, someone new to programming may subscribe to a mailing list or follow a newsgroup that discusses the computer language they want to learn. These groups are often composed of individuals with varying levels (novices, intermediaries, experts) of skill level forming what has been termed “communities of practice”. This legitimate peripheral participation or “lurking” is an acceptable and supported behaviour amongst many well established online communities. After reading the messages for a period of time novices may feel more comfortable and post questions of their own. This may lead to some form of debate amongst other participants in which new knowledge is co-created. Novices may contribute in other ways by sharing information related to issues they have already encountered. For example, the novice programmer may have been advised before participating in the message forum that using an integrated development environment (IDE) will aid their learning of how to program. Over time the community shares their experiences and members of all levels engage and learn from and with each other. This phenomena has been documented amongst mailing lists and newsgroups.
But what about the newer forms of social media such as Twitter?
Founded by social media expert and plain language writer Colleen Young (@colleen_young) the Health Care Social Media in Canada (hcsmca) Twitter-based community was designed as a means by which Canadians with an interest in social media within a health care context could exchange information. By posting tweets using the acronym, “hcsmca” those wanting to share and learn more about this topic area can follow the posts. Each week the community meets for a live tweet-up in which messages are exchanged in real time providing for a more conversational tone to the exchange. I have participated in this community almost since its inception. Over this time I have wondered about the types of connections that were being formed, what information is being shared and learned and how effective Twitter is as forms of information dissemination in this context.
To explore this further I examined the network relationships in the hcsmca community with NodeXL (http://nodexl.codeplex.com/). Using the import tool I limited the results to 100 people for this initial exploration. I requested edges (or connections) for each of these Twitter scenarios: “follows” relationship (an individual and their followers), “replies-to relationship in tweet” (a reply to an individual tweet), “mentions relationship in tweet” (a tweet that mentions a user) and a “tweet that is not a reply-to or mention” (a posted message or tweet). NodeXL calculates a variety of statistics related to network analysis. By using filters you can refine the resulting graph in form that provides meaning.
Image I provides one static representation of a many possible layouts of the results. The NodeXL tool allows for more dynamic views (e.g. colour coded relationships between users such as “follows”, “replies-to relationship in tweet” and depictions of the other metrics mentioned above). It also provides for the ability to re-position the location of each user. Image I (below) demonstrates one instance of these options.
Image I: Network analysis of #hcsmca community – November 26th, 2011
To better view the relationships I limited the out degree (people with the most connections) to seven. I then arranged the display from left to right by eigenvector centrality (a measure of importance in the network). Community leader Colleen Young, who often moderates the weekly tweet chats is positioned at the far left as she has the highest eigenvector centrality in this group. @DoctorFullerton is next, @nursefriendly and @ehealthmusings follow and so on. What may be of most interest are the two outliers positioned on the far right: @infoway and @jasonboies. They were represented in the graph because they had an out degree value greater than seven. However, I am curious as to why they had no connections to the remaining members in this particular snapshot of the #hcsmca community tweets. Does this indicate some form of lurking? How can this behaviour be explained?
In order to understand this further a content analysis of the tweets will be conducted. In the next installment I will explore the contents of these tweets using Netlytic (http://netlytic.org/), an Internet Community Text Analyzer.
Reference
Lave, J., & Wenger, E. (1991). Situated learning: legitimate peripheral participation. Cambridge [England] ; New York: Cambridge University Press.
Recommended Reading
Hansen, D. L., Schneiderman, B., & Smith, M. A. (2010). Analyzing social media networks with NodeXL: insights from a connected world. Burlington, MA: Morgan Kaufmann.
Valente, T. W. (2010). Social networks and health: models, methods, and applications. Oxford ; New York: Oxford University Press.
Thanks to @marc_smith for his assistance.
