Benoit, Kenneth 
ORCID: 0000-0002-0797-564X, Conway, Drew, Lauderdale, Benjamin E., Laver, Michael and Mikhaylov, Slava
(2016)
Crowd-sourced text analysis: reproducible and agile production of political data.
American Political Science Review, 110 (2).
278 - 295.
ISSN 0003-0554
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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 non-experts, 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.
| Item Type: | Article |
|---|---|
| Official URL: | http://journals.cambridge.org/action/displayJourna... |
| Additional Information: | © 2015 The Authors |
| Divisions: | Methodology |
| Subjects: | H Social Sciences > H Social Sciences (General) J Political Science > JA Political science (General) |
| Date Deposited: | 08 Jun 2015 16:02 |
| Last Modified: | 12 Dec 2024 01:04 |
| Projects: | 2011-StG 283794-QUANTESS, 2011-StG 283794-QUANTESS |
| Funders: | European Research Council, European Research Council |
| URI: | http://eprints.lse.ac.uk/id/eprint/62242 |
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