How We Do Things With Words: Analyzing Text as Social and Cultural Data

Dong Nguyen; Maria Liakata; Simon DeDeo; Jacob Eisenstein; David Mimno; Rebekah Tromble; Jane Winters

doi:10.3389/frai.2020.00062

How We Do Things With Words: Analyzing Text as Social and Cultural Data

Front Artif Intell. 2020 Aug 25:3:62. doi: 10.3389/frai.2020.00062. eCollection 2020.

Authors

Dong Nguyen^{1

2

3}, Maria Liakata^{1

4

5}, Simon DeDeo^{6

7}, Jacob Eisenstein⁸, David Mimno⁹, Rebekah Tromble¹⁰, Jane Winters¹¹

Affiliations

¹ Alan Turing Institute, London, United Kingdom.
² Institute for Language, Cognition and Computation, School of Informatics, The University of Edinburgh, Edinburgh, United Kingdom.
³ Department of Information and Computing Sciences, Utrecht University, Utrecht, Netherlands.
⁴ Department of Computer Science, University of Warwick, Coventry, United Kingdom.
⁵ School of Electronic Engineering and Computer Science, Queen Mary University of London, London, United Kingdom.
⁶ Department of Social and Decision Sciences, Carnegie Mellon University, Pittsburgh, PA, United States.
⁷ Santa Fe Institute, Santa Fe, NM, United States.
⁸ School of Interactive Computing, Georgia Institute of Technology, Atlanta, GA, United States.
⁹ Department of Information Science, Cornell University, Ithaca, NY, United States.
¹⁰ School of Media and Public Affairs, The George Washington University, Washington, DC, United States.
¹¹ School of Advanced Study, University of London, London, United Kingdom.

Abstract

In this article we describe our experiences with computational text analysis involving rich social and cultural concepts. We hope to achieve three primary goals. First, we aim to shed light on thorny issues not always at the forefront of discussions about computational text analysis methods. Second, we hope to provide a set of key questions that can guide work in this area. Our guidance is based on our own experiences and is therefore inherently imperfect. 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. This leads to our final goal: to help promote interdisciplinary collaborations. 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.

Keywords: computational social science; computational text analysis; cultural analytics; digital humanities; natural language processing.

Publication types

Review