Crowd-sourced Text Analysis: Reproducible and Agile Production of Political Data

By Kenneth Benoit, Drew Conway, Benjamin E Lauderdale, Michael Laver, Slava Jankin. 2016

Subfields: Natural Language Processing, Social Sciences & Humanities

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Citations in the corpus

ChatGPT outperforms crowd workers for text-annotation tasks (2023, background): “More typically, however, researchers have to conduct original annotations to ensure that the labels match their conceptual categories (Benoit et al., 2016).”
A Transformer-based Framework for Neutralizing and Reversing the Political Polarity of News Articles (2021, background): “This can be costly when dealing with a large quantity of data (where the biased content is shown to be quite sparse [13]).”
Can Exposure to Celebrities Reduce Prejudice? The Effect of Mohamed Salah on Islamophobic Behaviors and Attitudes (2019, methodology): “For more information on using Figure8 (formerly Crowdflower) to code data for training classifiers, see Benoit et al. (2016).”
Directions in Abusive Language Training Data: Garbage In, Garbage Out (2020, background): “The value of crowdsourcing lies in having annotations undertaken by ‘a large number of non-experts’ ([10], p. 278) – any bit of content can be annotated by multiple annotators, effectively trading quality for quantity.”
Party Positions from Wikipedia Classifications of Party Ideology (2019, unknown): “…assessments by large groups of nonexpert coders (i.e., crowd-coding) of the content of partymanifestos or parliamentary debates can give estimates of political position as good as assessments by expert coders (cf. Benoit et al. 2016; Haselmayer and Jenny 2017; Lehmann and Zobel 2018; Horn 2019).”
A Bad Workman Blames His Tweets? The Consequences of Citizens’ Uncivil Twitter Use When Interacting with Party Candidates (2016, methodology): “In combination with recent development in crowdsourcing techniques (Benoit et al., 2015), we believe our approach will enable researchers to answer standing questions in the study of political communication that up to now would require an extensive and expensive coding process.”
How Autocrats Manipulate Economic News: Evidence from Russia’s State-Controlled Television (2019, methodology): “We employed the crowdsourcingmethod that has been shown to work well in political science applications (Benoit et al. 2016).”
How to make causal inferences using texts (2018, unknown): “There are of course numerous other types of g functions that we might use—including latent scales (Slapin and Proksch, 2008), dictionary based counts of terms (Tausczik and Pennebaker, 2010), or crowd sourced measures of content (Benoit et al., 2016). Using the function g we are able to define a cau”