IDeaSInterpretive Data Science

Resources & Publications

What the community has produced, and what it builds on

The community's own publications, the readings that shaped its agenda, and pointers to tools for interpretive work with data. We link and annotate rather than host and maintain.

From our conferences

Work developed through IDeaS events has led to an edited volume and two journal special issues.

Foundational readings

The reading list the community assembled on its original website — the work that shaped its founding agenda.

  • Hannigan, T. R., Haans, R. F. J., Vakili, K., Tchalian, H., Glaser, V. L., Wang, M. S., Kaplan, S., & Jennings, P. D. (2019). Topic modeling in management research: Rendering new theory from textual data. Academy of Management Annals, 13(2), 586–632.The community's flagship paper, and the source of the rendering vocabulary above.
  • DiMaggio, P. (2015). Adapting computational text analysis to social science (and vice versa). Big Data & Society, 2(2).
  • Brayne, S. (2017). Big data surveillance: The case of policing. American Sociological Review, 82(5), 977–1008.
  • Bail, C. A., Brown, T. W., & Wimmer, A. (2019). Prestige, proximity, and prejudice: How Google search terms diffuse across the world. American Journal of Sociology, 124(5), 1496–1548.
  • Goldenstein, J., & Poschmann, P. (2019). Analyzing meaning in big data: Performing a map analysis using grammatical parsing and topic modeling. Sociological Methodology, 49(1), 83–131.
  • Nelson, L. K. (2019). Comment: To measure meaning in big data, don't give me a map, give me transparency and reproducibility. Sociological Methodology, 49(1), 139–143.A direct response to the paper above, and a statement of the transparency standard the community works to.
  • Mohr, J. W., & Bogdanov, P. (2013). Introduction—Topic models: What they are and why they matter. Poetics, 41(6), 545–569.
  • Schmiedel, T., Müller, O., & vom Brocke, J. (2019). Topic modeling as a strategy of inquiry in organizational research: A tutorial with an application example on organizational culture. Organizational Research Methods, 22(4), 941–968.
  • Rao, H., & Greve, H. R. (2018). Disasters and community resilience: Spanish flu and the formation of retail cooperatives in Norway. Academy of Management Journal, 61(1), 5–25.
  • Melamed, D., Simpson, B., Harrell, A., Munn, C. W., Abernathy, J. Z., & Sweitzer, M. (2020). Homophily and segregation in cooperative networks. American Journal of Sociology, 125(4), 1084–1127.
  • Wang, S., & Vergne, J.-P. (2017). Buzz factor or innovation potential: What explains cryptocurrencies' returns? PLOS ONE, 12(1), e0169556.

Tools & methods resources

Pointers to tools members of the community use. We deliberately link and describe rather than host and maintain code — keeping a code base alive is more work than a volunteer community can sustain.

Related centres

Research centres whose work connects with ours.

In development

We are building out a set of shared workflows — practical starting points for assembling the mix of tools that computational and interpretive research now requires, so that people are not each starting from scratch. If you have a workflow worth sharing, or want one, please get in touch.