In a previous post I presented an analysis of the tweets from the Health Care Social Media Canada (#hcsmca) Twitter community. By using a network analysis tool (NodeXL) I was able to determine that two Twitter identities (@infoway and @jasonboies) were participating but perhaps not in a connected way. When community members are “off to the side” it may be an indication of lurking behaviour (reading messages but not posting). However, since tweets were present from these Twitter accounts this label may not be applicable. A similar concept, labeled “legitimate peripheral participation” (described more thoroughly here) in which novices engage in a community of learners in limited fashion may be a more accurate descriptor of the phenomenon captured in the data set. In order to understand the findings from this network analysis a more thoroughly examination of the tweets containing referenced to the two outliers was required. To facilitate this process I used a tool called ITCA (Internet Community Text Analyzer) developed by Dr.Anatoliy Gruzd at Dalhousie University.
Using the Excel spreadsheet created by NodeXL from the network analysis I exported it into .cvs format, which was then imported into the ITCA tool. The dates of the tweets included Thursday November 24th, Friday November 25th and Saturday November 26th. There were 953 unique messages and 243 posters in this sample. The top ten posters (Image 1) is essentially in alignment with the network analysis, which was ordered by eigenvector centrality. In other words importance is, in part, reflected by the number of tweets.
Image 1: Top Ten Posters in #hcsmca Twitter community
The ‘local concepts’ (characters, words, terms and concepts) were extracted by looking for patterns frequently used in the data set. The ITCA tool revealed that there were 9812 unique terms. Image 2 shows the thirty most frequent terms and the number of times the term appear in the data set. The tag cloud formation shown in Image 2 also provides a visual representation of frequency (the larger the word the more times it appears). An individual term can be removed by clicking on the red X or explored further by clicking on its hypertext link, which reveals all instances by which has been tweeted.
Image 2: Top 30 Results of Local Concept Extractor (click to enlarge)
Using this tool I was able to search for the tweets associated with @Infoway. The results indicated that the two tweets were related to an upcoming HL7 (health level seven, a concept related to standardization in health information technology) certification. A hand search of the .cvs file indicated that one tweet on Friday November 25th, 2011 was directly from @infoway. The other was a re-tweet of this tweet by @alexanderberler on the same day. The second tweet was also recorded because @mentions were included in the data set obtained using NodeXL. Image 3 shows the @alexanderberler RT.
Image 3: @alexanderberler Re-tweet of @infoway tweet (click to enlarge)
A search of jasonboies revealed twelve tweets. Image 4 shows the total number of times in which tweets contained this Twitter identity in this data set.
Image 4: Incidents of jasonboies
Tweets with jasonboies appear to have taken place from Friday November 25th (four in early evening UTC) to Saturday November 26th (eight in late evening UTC). This time frame is outside the weekly hcsmca tweet chat, which took place in the evening on Thursday November 24th (the weekly tweet chat is held every Wednesday at 1:00 pm EST except for the last week of the month in which it is held on Thursday evenings).
Based on this preliminary analysis it would appear as though connecting with other members of the hcsmca community is a phenomenon beyond just using the hashtag in your tweet. These findings may indicate that being engaged means participating with others in the real time chat.
Perhaps more importantly this analysis demonstrates the need to examine not only the pattern of tweets as yielded using network analysis tools but also to examine the content. In addition, these findings should be interpreted with the aid of survey data and interview findings obtained directly from members of hcsmca community. For example, a survey could determine which participants are tweeting as part of their work, which may affect which time of the day they use Twitter. Interviews would provide even richer detail allowing us to understand what exactly prompts someone to both tweet and re-tweet material in the hcsmca community.
Recommended reading
Daniel, B. K. (2010). Handbook of research on methods and techniques for studying virtual communities: paradigms and phenomena. Hershey, PA: Information Science Reference.
Feldman, R., & Sanger, J. (2007). The text mining handbook: advanced approaches in analyzing unstructured data. Cambridge ; New York: Cambridge University Press.
