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Scholars are increasingly utilizing online workforces to encode latent political concepts embedded in written or spoken records. In this letter, we build on past efforts by developing and validating a crowdsourced pairwise comparison framework for encoding political texts that combines the human ability to understand natural language with the ability of computers to aggregate data into reliable measures while ameliorating concerns about the biases and unreliability of non-expert human coders. We validate the method with advertisements for U.S. Senate candidates and with State Department reports on human rights. The framework we present is very general, and we provide free software to help applied researchers interact easily with online workforces to extract meaningful measures from texts.
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Carlson et al. (2017) studied this question.
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