Skip to main content

Tracking knowledge exchange events from social media and scholarly usage data

With research communication and more importantly usage moving online we have unprecedented access to data that can inform us on how knowledge and information travel from their point of dissemination to communities of use. In particular, social media and article usage data provide a rich window on who is looking at what and when. What has not been studied to date is how to effectively use the large scale data available to understand how these processes link up.

This project will utilize large scale data on downloads and social media activity around journal articles from project partners in the publishing industry. Treating each of these data sources as a single time domain channel we will identify cross-channel correlations. These correlations can provide a route into understanding the processes by which information and knowledge is transmitted, in turn providing insight into effect engagement and communication strategies for researchers.

This project would suit a student with an interest in research communications and/or signal processing. A computing background and experience with data science would be an advantage.