Nathan TeBlunthuis
Biographic Data
| ID | 6847858 |
|---|---|
| NAME | Nathan TeBlunthuis |
| GIVEN NAMES | Nathan |
| FAMILY NAME | TeBlunthuis |
| SIGNATURE | TEBLUNTHUIS N |
| AFFILIATIONS | Northwestern University |
| ORCID | 0000-0002-3333-5013 |
| VERIFIED | Yes |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 3 |
| AUTHOR COUNT | 2 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2024 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 1 |
Critical, but constructive: Defining, detecting, and addressing bias in Computational Social Science
Misclassification in Automated Content Analysis Causes Bias in Regression. Can We Fix It? Yes We Can
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. They 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 when input to downstream statistical analyses–…
Misclassification in Automated Content Analysis Causes Bias in Regression. Can We Fix It? Yes We Can
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. They 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 when input to downstream statistical analyses–…
Misclassification in Automated Content Analysis Causes Bias in Regression. Can We Fix It? Yes We Can
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. They 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 when input to downstream statistical analyses–…
Critical, but constructive: Defining, detecting, and addressing bias in Computational Social Science
Computational and Text Analysis Methods (2 works) · Artificial Intelligence (1 works) · Categorization (1 works) · Computational model (1 works) · Computational sociology (1 works) · Computer Science (1 works) · Contemporary Sociological Theory and Practice (1 works) · Data Analysis with R (1 works) · Data mining (1 works) · Key (lock (1 works)