Dr. Frédérique Bone is a research fellow at SPRU (Science Policy Research Unit) at the University of Sussex. Frédérique holds a PhD in entrepreneurship and innovation economics from the BETA unit at the University of Strasbourg. Frédérique has a longstanding experience in using quantitative and qualitative methods independently or as a mix to study science and technological evolution in the biotech sector, and more recently Artificial Intelligence (AI) technologies and mass production.

For the Deep Transition project, she is working on new methods to trace technological change and rules. Together with Dr Rotolo, Frédérique is using the latest web scraping, data science, and natural language processing techniques to trace rules of mass production in bespoke text corpora of two newspapers from 1850s to the present (i.e. Scientific American, and the New York Times). The development of this new methodology aims to enable the quantitative study of both the technical and social aspects of technologies over long time periods.

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Blog - November 23, 2020

How to Identify Rules from Text Mining

Technologies can shape society and individual behaviours. Historical data helps to understand how different socio-economic actors within society, can influence the emergence of new technologies within and across different socio-technical systems (e.g. mobility, food, energy). Traditional historical work on understanding technological evolution and diffusion has relied on examining archives in considerable detail, but this meant…

Episode 4: Diving in Deep to the Data: Text Mining and Deep Transitions

Deep Transitions
Episode 4: Diving in Deep to the Data: Text Mining and Deep Transitions
What can over 3.5 million pages of the New York Times, and 2000 issues of Scientific American, tell us about the social changes witnessed from 1850 to the present day? In this week’s episode, Diving in Deep to the Data: Text Mining and Deep Transitions, Frederique Bone and Daniele Rotolo tell us how they converted…