Original image by Dean Terry

Improving the Social Network Analysis Methodology

We have been collecting data for just shy of a year now and have been developing the Twitter social network analysis methodology for a little longer than that. As you might recall, we have been following the Mobile Health conversation via the #mHealth conversation and have finalised the collection of those data. The processing is almost finished and we can now progress to the next stage of ethnography to further understand what we have collected.

We have been improving the methodology as we go and the last time we received some assistance was to write some code for the Gephi program. Recently, we have been talking with colleagues from the University of Wollongong’s SMART Infrastructure faculty, who have been developing the collection process of Twitter data. Their project is related to flooding information in Indonesia (CogniCity), however Tom Holderness has been kind enough to share his work on GitHub.

When we install this JavaScript, which uses NodeJS, we will have an automated version of the manual process we have been struggling with for the past year. Further, the code is customisable so the researcher can  query the Twitter Stream API for the specific data they require. You can read more about the CogniCity NodeJS application on GitHub.

If we can improve the processing speed further, we will have a research prototype that can be shared with other researchers who are interested in Twitter social network analysis – hopefully a post soonish will reveal this!