My doctoral efforts on a thesis about social change has led me to the edges of the network analysis world, and how practical applications of it can be utilized to improve our understanding of social media relationships.
It is exciting to read through all the social media tools and strategies available to provide the necessary social interaction for a positive learning experience. However, as the digital age has compounded in amazing capacities of insights, connectivity, and opportunities, it is important that there is an ability to chart the relationships that occur within social media in order to make improvements to educational uses and approaches. Derek Hansen published a research paper in On the Horizon this year thatÂ provides context in the effort of successfully exploring those relationships, as well as how to systemically analyze social media activity. The specific finding of this article involves the use of a Microsofft Excel plug-in called NodeXL, which provides data manipulation in user friendly spreadsheets directly from the raw data from social media tools. Non-technical graduate students were able to successfully extract valuable insights into the network analysis of social media relationships with minimal training (in context of the training required to analyze without the plug-in). Since social interaction maps extremely well to networking concepts, using network analysis techniques allow the tracking and impact of social behavior without the requirement of manually reading each interaction (post, video, comments, tweet, etc.).
While potential for applying network analysis to social media relationships is immense, there is the natural limitation that network analysis techniques only analyze the data, not the quality of the data. As such, selection bias and triangulation are absolutely critical to the successful utilization of those techniques. However, the value of systemic analysis makes it well worth the effort. The research demonstrated used NodeXL to track and analyze both Twitter on the topic of open education and YouTube on the topic of surgery. The analysis of the Twitter topic provided the insight on how a specific member they followed would seek more iniatives on that topic, as well as where and would they would start. This ensures strategic networking. Analysis on the YouTube topic clearly indicated where professional videos were lacking in specific surgerical subjects, where there was a great deal of accurate information on cosmetic surgery. This provides insight to the educational community that students can be directed to cosmetic surgery collections. These analyses can certainly be completed manually, but group clustering available with NodeXL gives a better holistic perspective.
RelevanceÂ of social media relationships is especially strong for educational researchers and instructional designers who need to understand the relationships between social learning and educational outcomes especially in online settings. While experts could manipulate relational data, tools like NodeXL provide possibilities of analysis and subsequent improvement of social media approaches in curriculum for non-technical individuals. Network analysis is a well established field, but applying it for practical uses is not nearly as formed. Developing tools like NodeXL will assist greatly in that capability of practical application.
Hansen, D. (2011). Exploring social media relationships. On The Horizon, 19(1), 43-51.