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Toward a framework for creating trustworthy measures with supervised machine learning for text
15 references·1 citing·51 second hop
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References

  • 2013Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts3,290 cited
  • 2017Text Preprocessing For Unsupervised Learning: Why It Matters, When It Misleads, And What To Do About It466 cited
  • 2016Insecure Majorities453 cited
  • 2021(Almost) Everything in Moderation: New Evidence on Americans' Online Media Diets341 cited
  • 2019Polarization281 cited
  • 2021Word Embeddings: What Works, What Doesn’t, and How to Tell the Difference for Applied Research249 cited
  • 2020Political Advertising Online and Offline232 cited
  • 2023Text as Data: A New Framework for Machine Learning and the Social Sciences204 cited
  • 2012Improving Predictions using Ensemble Bayesian Model Averaging158 cited
  • 2021Post Post-Broadcast Democracy? News Exposure in the Age of Online Intermediaries85 cited
  • 2021Topics, Concepts, and Measurement: A Crowdsourced Procedure for Validating Topics as Measures65 cited
  • 2017A Pairwise Comparison Framework for Fast, Flexible, and Reliable Human Coding of Political Texts45 cited
  • 2019Measuring Polarization with Text Analysis: Evidence from the UK House of Commons, 1811–201540 cited
  • 2019Understanding Delegation Through Machine Learning: A Method and Application to the European Union34 cited

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  • 2020Human Rights are (Increasingly) Plural: Learning the Changing Taxonomy of Human Rights from Large-scale Text Reveals Information Effects25 cited
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    • 2026High accuracy with low costs: the pretrain-finetune paradigm for classification with transformer-based language models1 cited