The Augmented Social Scientist

Using Sequential Transfer Learning to Annotate Millions of Texts with Human-Level Accuracy

Bibliographic Data

ID2331135
AuthorsSalomé Do (0000-0002-6095-6253, ENS-Paris/PSL (LATTICE), Paris, France), E Ollion (0000-0003-3099-5240, Institut Polytechnique de Paris (CREST), Palaiseau, France, corresponding author), Rubing Shen (0000-0002-5504-6108, Sciences Po (Medialab), Paris, France)
Year2024
Volume53
Issue3
Pages1167-1200
Publication date2024-08-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueSociological Methods & Research (JOURNAL)
Journal identifiersISSN: 0049-1241 • E-ISSN: 1552-8294
PublisherSAGE Publications Inc (PUBLISHER)
DOI10.1177/00491241221134526
OpenAlexW4311510002
LanguageEN
Citations received19
References cited33

The last decade witnessed a spectacular rise in the volume of available textual data. With this new abundance came the question of how to analyze it. In the social sciences, scholars mostly resorted to two well-established approaches, human annotation on sampled data on the one hand (either performed by the researcher, or outsourced to microworkers), and quantitative methods on the other. Each approach has its own merits - a potentially very fine-grained analysis for the former, a very scalable one for the latter - but the combination of these two properties has not yielded highly accurate results so far. Leveraging recent advances in sequential transfer learning, we demonstrate via an experiment that an expert can train a precise, efficient automatic classifier in a very limited amount of time. We also show that, under certain conditions, expert-trained models produce better annotations than humans themselves. We demonstrate these points using a classic research question in the sociology of journalism, the rise of a 'horse race' coverage of politics. We conclude that recent advances in transfer learning help us augment ourselves when analyzing unstructured data

Annotation · Big data · Classifier (UML) · Data mining · Data science · Machine learning · Scalability · Transfer of learning · Artificial Intelligence · Computational and Text Analysis Methods · Computer Science · Sentiment Analysis and Opinion Mining · Topic Modeling