Publication Type
Journal Article
Version
acceptedVersion
Publication Date
5-2016
Abstract
Empirical social science often relies on data that are not observed in the field, but are transformed into quantitative variables by expert researchers who analyze and interpret qualitative raw sources. While generally considered the most valid way to produce data, this expert-driven process is inherently difficult to replicate or to assess on grounds of reliability. Using crowd-sourcing to distribute text for reading and interpretation by massive numbers of nonexperts, we generate results comparable to those using experts to read and interpret the same texts, but do so far more quickly and flexibly. Crucially, the data we collect can be reproduced and extended transparently, making crowd-sourced datasets intrinsically reproducible. This focuses researchers’ attention on the fundamental scientific objective of specifying reliable and replicable methods for collecting the data needed, rather than on the content of any particular dataset. We also show that our approach works straightforwardly with different types of political text, written in different languages. While findings reported here concern text analysis, they have far-reaching implications for expert-generated data in the social sciences.
Discipline
Models and Methods | Political Science
Research Areas
Political Science
Publication
American Political Science Review
Volume
110
Issue
2
First Page
278
Last Page
295
ISSN
0003-0554
Identifier
10.1017/S0003055416000058
Publisher
Cambridge University Press
Citation
BENOIT, Kenneth, CONWAY, Drew, LAUDERDALE, Benjamin E., LAVER, Michael, & MIKHAYLOV, Slava.(2016). Crowd-sourced text analysis: Reproducible and agile production of political data. American Political Science Review, 110(2), 278-295.
Available at: https://ink.library.smu.edu.sg/soss_research/3970
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1017/S0003055416000058