Publication: The Structure and Characteristics of #PhDChat
One of the highlights of academia is working closely with students and seeing them grow, take on challenges, struggle, and create meaningful change in the world. This happens in classrooms, on the web, in design/development projects, in research endeavors, and so on. Kasey Ford, who was one of my advisees, recently completed her MA thesis examining #PhDChat, an online social network, and we have published a study out of that work in the Journal of Interactive Media in Education. I’m excited to share the abstract below:
#PhDChat is an online network of individuals that has its roots to a group of UK doctoral students who began using Twitter in 2010 to hold discussions. Since then, the network around #PhDchat has evolved and grown. In this study, we examine this network using a mixed methods analysis of the tweets that were labeled with the hashtag over a one-month period. Our goal is to understand the structure and characteristics of this network, to draw conclusions about who belongs to this network, and to explore what the network achieves for the users and as an entity of its own. We find that #PhDchat is a legitimate organizational structure situated around a core group of users that share resources, offer advice, and provide social and emotional support to each other. Core users are involved in other online networks related to higher education that use similar hashtags to congregate. #PhDchat demonstrates that (a) the network is in a continuous state of emergence and change, and (b) disparate users can come together with little central authority in order to create their own communal space.
Ford, K., Veletsianos, G., & Resta, P. (2014). The Structure and Characteristics of #PhDChat, an Emergent Online Social Network. Journal Of Interactive Media In Education, 18(1). Retrieved April 16, 2014, from http://www-jime.open.ac.uk/jime/article/view/2014-08
Below is a visualization of users mentioning #PhDChat, with users grouped into clusters. Users with frequent or exclusive ties, represented in this study as replies and mentions, are clustered together. Thus, each cluster represents users that are most closely associated to one another based on their frequency of interactions.