Text as Data

Textual data abound on human communication. We leave textual traces on a great variety of our everyday doings. Our intimate concerns are formulated in google searches, we coordinate community initiatives in Facebook groups and articulate political ideologies on social media platforms. Text has become a fundamental medium through which many people interact, express and position themselves. In Digital Society the analysis, categorization and organization of textual content is off out most importance to governments, businesses, the press and academics alike. The field of computational text analysis is one of the central fields within the wider data revolution. Many methods are being imported into the social science from other fields, especially computer science. But concerns with text models biases and interpretative validity is becoming a growing concern within academia and beyond. This lectures series starts from the premise that text is not just data but social data. Texts are tied to specific contexts and cultural practices, properties that constitutes text data big potential, but also its high risk of being misinterpreted and misclassified - ruining  both interpretation and measurement. More generally textual data presents challenges that ranges from core concerns within machine learning, to deep methodological issues in the social science regarding quantification, interpretation and how to combine qualitative and quantitative modes of analysis. In this lecture series we have invited scholars how have made valuable contributions to the interdisciplinary field of computational text analysis.


Participative Epistemology in Social Data Science: combining ethnography with computational and statistical approaches 

The third speaker is Gian Marco Campagnolo who is Lecturer in Science & Technology Studies and Turing Inaugural Fellow at the University of Edinburgh. Developed through a number of inter-linked ESRC funded projects in the last five years, Gian Marco Campagnolo's research is the social study of data science. He recently completed a ten year long research programme on the sociology of business knowledge applying a mixed methods approach to identify the dynamics of expertise in information technology markets. His new research programme focuses on valuation practices in high-velocity environments looking at the use of data science in sport.

Abstract 

In this lectureI introduce the new notion of participative epistemology and discuss how it can contribute to sensitising social data science. I will do so by offering the case of a project where ethnographic, computational and sequence analysis methods have been used in combination. By exemplifying it through the analysis of the project’s research design and early results, I suggest a new view on the collaboration between data science and social science. The new notion of participative epistemology will be articulated in three points: (1) a view of how research subjectivities can shape research design; (2) an attention for the research object as co-participant in the research and (3) a sensitivity for the object-relation regimes as having agency in shaping research outcomes.

The lecture will take place in building 1, 2ndfloor, room 26 (1.2.26) of the CSS Campus, University of Copenhagen, from 11.00 am to 12.30 pm.

If you have questions or want to know more, please write Sophie Smitt Sindrup Grønning at sophie.groenning@sodas.ku.dk.