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 analyse 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. Paper available via http://eprints.lse.ac.uk/62242/

Kenneth Benoit is currently Professor of Quantitative Social Research Methods, and Head of the Department of Methodology at the London School of Economics and Political Science. He is also Part-Time Professor in the Department of Political Science at Trinity College Dublin, and has previously held a position at the Central European University (Budapest). He received his Ph.D. (1998) from Harvard University, Department of Government. His current research focuses on automated, quantitative methods for processing large amounts of textual data, mainly political texts and social media.

Lunch will be provided at the seminar after the Q&A session.