1 Dec 2022 17:00 - 19:00 Room SG1, Alison Richard Building, Sidgwick Site, Cambridge

Description

With technologies that afford much larger scale data collection than previously imagined, new ways of handling, processing and interpreting this data are called upon. This is especially pertinent when analysing textual data. Qualitative approaches to the analysis of text can attend to social worlds in their complexity, acknowledge contradiction and nuance, and provide contextualised interpretations of thick concepts and social processes. However, these do not always translate so easily to large scale datasets and computational approaches.

Computational approaches offer scalability but are necessarily reductive, and so demand interrogation and criticality in the interpretation of their outputs. As acknowledged in critical and feminist data studies, data is not neutral, and from collection and analysis through to how we represent and report on data, mindfulness of this non-neutrality should be employed. In this talk, I will present research that strives to balance scalability with qualitative insight; consider ways of working with computational methods in a critical and reflexive way; and reflect on how interdisciplinary approaches and hybrid methodologies can support this.

About the speaker

Shauna Concannon is an assistant professor in Computer Science & Digital Humanities at Durham University. Shauna’s work focuses on the societal and ethical implications of communication technologies. Taking an interdisciplinary approach, they apply and critique computational approaches, often in an attempt to understand how knowledge is linguistically encoded in an increasingly technologically-mediated society.

Shauna started her academic life in the humanities, completing a masters in modernist literature before completing a PhD on deliberation in computer-mediated dialogue in the Computational Linguistics Lab, Queen Mary University of London. More recently, they completed postdoctoral research at the Universities of Cambridge, York and Newcastle, working on intersectional feminist approaches to data science, human-data-interaction and human-machine-communication.


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