TRS.20.037 – Measuring Science Communication in the Big Data era for resilient societies
The communication of science to society is a fundamental pillar for resilient and science-based societies. Science communication represents a unique space for science-society interactions, including both old and new forms of (online and social) media communication and interaction. However, science communication is experiencing an unprecedented transformation. The rise of massive digitalization, big data and networked communication technologies, accompanied of an increasing plurality of actors engaging in the communication and exchange of scientific results (e.g. journalists, researchers, influencers, bots, etc.), run the risk of challenging the productive dissemination and societal reception of scientific knowledge. This problem is exacerbated by the critical absence of tools and approaches to systematically measure and monitor the most important effects of science communication on society, including dynamics related to successful but also unsuccessful or negative practices (e.g. misinformation, fake news, etc.). The initiative aims at combining theory and practice to develop approaches to measure and improve the quality of science communication. This initiative will move beyond the state-of-the-art by integrating existing and novel big data sources (scientometrics, altmetrics, and online media) and advanced data analytics (social network analysis, machine learning) into a rich set of measurements to monitor science communication dynamics and societal interactions. The initiative will target to inform science communicators, policy-makers, and other societal stakeholders about the complex social, political, and economic factors inherent in science communication, as well as to promote opportunities for improving science communication by creating knowledge through measurement. This initiative can only be realized by the joint effort of a broad consortium of both academic and non-academic partners, each with deep and diverse knowledge in terms of both practical experience and multidisciplinary academic perspectives (science communication, sociology, statistics, political science, citizen science, research policy, STS, and computer sciences) and relevant societal stakeholders, including science communicators, journalists and citizen scientists.
altmetrics, Big Data, citizen science, data visualization, machine learning, public understanding of science, science communication, science journalism, science-society interaction, scientometrics, social media metrics
Delft Institute of Applied Mathematics, Technische Universiteit Delft (TUD)
|Organisation||Leiden University (LEI)|
|Name||Dr. R. (Rodrigo) Costas|