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Merge branch 'master' of https://git.overleaf.com/62a956eb9b9254783cc84c82 into osf

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Nathan TeBlunthuis
2023-03-02 13:17:42 -08:00
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@@ -109,8 +109,6 @@ Automated Content Analysis; Machine Learning; Classification Error; Attenuation
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\abstract{
%We show how automated classifiers (ACs), even biased ACs without high accuracy, can be statistically useful in communication research.
Automated classifiers (ACs), often built via supervised machine learning (SML), can categorize large, statistically powerful samples of data ranging from text to images and video, and have become widely popular measurement devices in communication science and related fields.
Despite this popularity, even highly accurate classifiers make errors that cause misclassification bias and misleading results in downstream analyses—unless such analyses account for these errors.