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Machine Learning and Grounded Theory Method: Convergence, Divergence, and Combination

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Date
2016
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Source
Proceedings of the 2016 ACM International Conference on Supporting Group Work
Publisher
Association for Computing Machinery
Abstract
Grounded Theory Method (GTM) and Machine Learning (ML) are often considered to be quite different. In this note, we explore unexpected convergences between these methods. We propose new research directions that can further clarify the relationships between these methods, and that can use those relationships to strengthen our ability to describe our phenomena and develop stronger hybrid theories.
Description
Muller, Michael; Guha, Shion; Baumer, Eric P.S.; Mimno, David; Shami, N. Sadat (2016): Machine Learning and Grounded Theory Method: Convergence, Divergence, and Combination. Proceedings of the 2016 ACM International Conference on Supporting Group Work. DOI: 10.1145/2957276.2957280. Association for Computing Machinery. pp. 3–8. Sanibel Island, Florida, USA
Keywords
supervised learning , coding families , machine learning , axial coding , unsupervised learning , grounded theory
Citation
DOI
10.1145/2957276.2957280
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GROUP 2016