Researchers Develop Method to Measure Human Values in Social Media Using Calibrated AI Models
Computer scientists have created a framework for using large language models (LLMs) to identify and measure expressions of human values in social media texts across multiple languages. The approach combines Schwartz's theory of basic human values with iterative prompt calibration and expert verification to improve accuracy and reduce misinterpretations. This work enables scalable analysis of subjective cultural and personal values in naturally occurring online discourse.
Researchers from arXiv's computer science community have developed a methodology for measuring how human values are expressed in social media posts using large language models. The study, grounded in Schwartz's established theory of basic human values, tests how different LLMs, prompts, and instruction languages interpret value expressions in non-English texts. The team found that different models produce varying interpretations of the same content, but through iterative prompt calibration and error analysis, they could reduce misattributions and improve alignment with expert annotations. Beyond standard metrics like precision and recall, the researchers evaluated structural alignment between values, error patterns, and annotation stability. The final innovation involves transferring LLM annotations to encoder models through soft-label training, which preserves both the theoretical grounding and uncertainty information about value expression, enabling practical large-scale applications.
What's missing
The study's limitations regarding potential biases in LLM training data, generalizability to non-social media contexts, and the specific performance metrics (precision, recall, F1 scores) for individual value categories are not detailed in the abstract.
What different sources said
- arXiv cs.CLCenter
Measuring Human Value Expression in Social Media Texts: Calibrated LLM Annotation and Encoder Transfer
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