Computer-Assisted Text Analysis for Comparative Politics

Abstract

Recent advances in research tools for the systematic analysis of textual data are enabling exciting new research throughout the social sciences. For comparative politics, scholars who are often interested in non-English and possibly multilingual textual datasets, these advances may be difficult to access. This article discusses practical issues that arise in the processing, management, translation, and analysis of textual data with a particular focus on how procedures differ across languages. These procedures are combined in two applied examples of automated text analysis using the recently introduced Structural Topic Model. We also show how the model can be used to analyze data that have been translated into a single language via machine translation tools. All the methods we describe here are implemented in open-source software packages available from the authors.

Public perceptions of air pollution and climate change: different manifestations, similar causes, and concerns

“…Notably, this method permits analysis of response content alongside other variables such as gender, age, and experimental treatment variables (Roberts et al 2014b). Besides the analysis of open-ended survey questions on climate change (Tvinnereim and Fløttum 2015), STM has so far been applied to the analysis of documents produced by organizations opposed to action on climate change (Farrell 2016), classification of Arab Muslim cleric writings, and reactions to the Edward Snowden case (Lucas et al 2015). Related topic modeling methods have been employed to classify think tank statements on climate science and policy (Boussalis and Coan 2016) as well as analyze trends in newspaper coverage of nuclear technology over time (Jacobi et al 2016),…”

Terror Management and Twitter: The Case of the 2016 Berlin Terrorist Attack

“…To fit a model that best represents the data given our attempt to gain insights about Twitter usage during and after terrorist attacks, we fitted four different models with 10, 20, 30, and 40 topics. To determine the best model, we then calculated semantic coherence and exclusivity, which are measures that quantify necessary statistical properties: semantic coherence is higher if more probable words in a topic frequently co-occur together (Mimno et al, 2011), whereas exclusivity is higher if more words are exclusive to their corresponding topics (Lucas et al, 2015). Figure A2 in Appendix A illustrates the average and median values for both measures and all four models.…”

Human Rights Texts: Converting Human Rights Primary Source Documents into Data

“…Unsupervised statistical learning tools also exist, which are useful for revealing other patterns within the human rights document corpus [7,[13][14][15][16] without reference to the existing coded human rights variables, which we describe below. These tools are more generally part of the emergent field of computational social science or "big data" analysis [17][18][19] of which there are several recent examples in the study of human rights [1,10,[20][21][22][23] and many other examples from political science and social science more generally [11,[24][25][26][27][28].…”