Interdisciplinary insights and partnerships are essential for realizing the full potential of any computational text analysis involving social and cultural concepts, and the more we bridge these divides, the more fruitful we believe our work will be. This leads to our final goal: to help promote interdisciplinary collaborations. Still, given our diversity of disciplinary backgrounds and research practices, we hope to capture a range of ideas and identify commonalities that resonate for many. Our guidance is based on our own experiences and is therefore inherently imperfect. Second, we hope to provide a set of key questions that can guide work in this area.
First, we aim to shed light on thorny issues not always at the forefront of discussions about computational text analysis methods. In this article we describe our experiences with computational text analysis involving rich social and cultural concepts. 11School of Advanced Study, University of London, London, United Kingdom.10School of Media and Public Affairs, The George Washington University, Washington, DC, United States.9Department of Information Science, Cornell University, Ithaca, NY, United States.8School of Interactive Computing, Georgia Institute of Technology, Atlanta, GA, United States.7Santa Fe Institute, Santa Fe, NM, United States.6Department of Social and Decision Sciences, Carnegie Mellon University, Pittsburgh, PA, United States.5School of Electronic Engineering and Computer Science, Queen Mary University of London, London, United Kingdom.4Department of Computer Science, University of Warwick, Coventry, United Kingdom.3Department of Information and Computing Sciences, Utrecht University, Utrecht, Netherlands.2Institute for Language, Cognition and Computation, School of Informatics, The University of Edinburgh, Edinburgh, United Kingdom.1Alan Turing Institute, London, United Kingdom.Dong Nguyen 1,2,3 *, Maria Liakata 1,4,5, Simon DeDeo 6,7, Jacob Eisenstein 8, David Mimno 9, Rebekah Tromble 10 and Jane Winters 11
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