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cs.CL2025
Large Language Model Hacking: Quantifying the Hidden Risks of Using LLMs for Text Annotation
Joachim Baumann, Paul Röttger, Aleksandra Urman +4
Large language models are rapidly transforming social science research by enabling the automation of labor-intensive tasks like data annotation and text analysis. However, LLM outp…
cs.CL2022★ 5 cited
Panning for gold: Lessons learned from the platform-agnostic automated detection of political content in textual data
Mykola Makhortykh, Ernesto de León, Aleksandra Urman +5
The growing availability of data about online information behaviour enables new possibilities for political communication research. However, the volume and variety of these data ma…