Measuring Human Value Expression in Social Media Texts: Calibrated LLM Annotation and Encoder Transfer

arXiv:2606.11018v2 Announce Type: replace Abstract: Measuring subjective constructs in naturally occurring social media text requires annotation procedures that are theoretically grounded, empirically validated, and transferable to an encoder model for scalable prediction. Using non-English social media posts annotated according to Schwartz's theory of basic human values, we investigate how different LLMs, prompts, and instruction languages operationalize the expression of values in text. We argue that although texts may permit multiple plausible interpretations, theory-based value definitions can constrain interpretations and reduce spurious value attributions. Beyond precision, recall, and F1, we evaluate structural alignment between values, error structure, confidence-ambiguity relations, and annotation stability. We show that different LLMs produce different value interpretations. Iterative prompt calibration through error analysis reduces misattributions and improves alignment with expert annotations. We also derive targeted expert verification rules from recurrent error structures and use them during corpus annotation. We show that LLM annotations can be transferred to an encoder model through soft-label training, retaining theory-based value interpretations and information about uncertainty in value expression. Finally, using sensitivity analysis, we show that annotation bias propagates into large-scale predictions, but standardized trends and major event responses are more robust.

Abstract:Measuring subjective constructs in naturally occurring social media text requires annotation procedures that are theoretically grounded, empirically validated, and transferable to an encoder model for scalable prediction. Using non-English social media posts annotated according to Schwartz's theory of basic human values, we investigate how different LLMs, prompts, and instruction languages operationalize the expression of values in text. We argue that although texts may permit multiple plausible interpretations, theory-based value definitions can constrain interpretations and reduce spurious value attributions. Beyond precision, recall, and F1, we evaluate structural alignment between values, error structure, confidence-ambiguity relations, and annotation stability. We show that different LLMs produce different value interpretations. Iterative prompt calibration through error analysis reduces misattributions and improves alignment with expert annotations. We also derive targeted expert verification rules from recurrent error structures and use them during corpus annotation. We show that LLM annotations can be transferred to an encoder model through soft-label training, retaining theory-based value interpretations and information about uncertainty in value expression. Finally, using sensitivity analysis, we show that annotation bias propagates into large-scale predictions, but standardized trends and major event responses are more robust.

Computation and Language (cs.CL)

arXiv:2606.11018 [cs.CL]

(or arXiv:2606.11018v2 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2606.11018 Focus to learn more arXiv-issued DOI via DataCite

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Measuring Human Value Expression in Social Media Texts: Calibrated LLM Annotation and Encoder Transfer

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