# Computationally Intensive Research: Advancing a Role for Secondary Analysis of Qualitative Data

arXiv: https://arxiv.org/abs/2506.04230 · HTML: https://mlsanity.com/paper/2506.04230/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 149 references that are in the map.
- First posted on arXiv 15 Jan 2025.

## Builds on (most cited first, 3 of 3)

- [Top2Vec: Distributed Representations of Topics](https://mlsanity.com/paper/2008.09470/) (2020)
- [Fuzzy Approach Topic Discovery in Health and Medical Corpora](https://mlsanity.com/paper/1705.00995/) (2017)
- [Unwanted Advances in Higher Education: Uncovering Sexual Harassment Experiences in Academia with Text Mining](https://mlsanity.com/paper/2001.11552/) (2019)

## Abstract (arXiv)

This paper draws attention to the potential of computational methods in reworking data generated in past qualitative studies. While qualitative inquiries often produce rich data through rigorous and resource-intensive processes, much of this data usually remains unused. In this paper, we first make a general case for secondary analysis of qualitative data by discussing its benefits, distinctions, and epistemological aspects. We then argue for opportunities with computationally intensive secondary analysis, highlighting the possibility of drawing on data assemblages spanning multiple contexts and timeframes to address cross-contextual and longitudinal research phenomena and questions. We propose a scheme to perform computationally intensive secondary analysis and advance ideas on how this approach can help facilitate the development of innovative research designs. Finally, we enumerate some key challenges and ongoing concerns associated with qualitative data sharing and reuse.