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arxiv: 2606.11018 · v1 · pith:GNI3MCI3new · submitted 2026-06-09 · 💻 cs.CL

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

Maria Milkova , Maksim Rudnev This is my paper

Pith reviewed 2026-06-27 13:11 UTC · model grok-4.3

classification 💻 cs.CL
keywords human valuessocial media annotationLLM calibrationencoder transfersoft-label trainingSchwartz theoryvalue expressionuncertainty preservation
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The pith

Calibrated LLM annotations of human values transfer to encoder models while retaining uncertainty information.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper tests whether large language models can annotate social media posts for expressions of basic human values according to a fixed psychological theory, then pass those labels to a smaller encoder model for wider use. It compares different models and prompts, corrects recurring errors through calibration, and checks that the results stay aligned with expert judgments while tracking how ambiguous each value expression is. A reader would care because this combination aims to make reliable, theory-grounded measurement of subjective content feasible at the scale of everyday online text.

Core claim

The authors establish that LLM annotations of value expressions in non-English social media posts, after iterative prompt calibration and error-based expert rules, transfer to an encoder model through soft-label training. This transfer retains the theory-based interpretations of values and preserves information about uncertainty in value expression. Evaluations cover structural alignment between values, error patterns, confidence relations, and annotation stability across models and languages.

What carries the argument

soft-label training on calibrated LLM annotations guided by Schwartz's theory of basic human values

If this is right

  • Iterative prompt calibration through error analysis reduces misattributions and improves structural alignment with expert annotations.
  • Different LLMs produce distinct value interpretations that require model-specific calibration.
  • Soft-label training allows encoder models to scale predictions while keeping both theory alignments and uncertainty signals.
  • Recurrent error structures can be turned into targeted expert verification rules for corpus annotation.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The method could apply to other subjective constructs in text if similar theory-based constraints are available.
  • Preserved uncertainty information might support selective human review or active learning loops in larger annotation projects.
  • Testing the transfer on additional languages or platforms would clarify how language-specific the calibration steps need to be.

Load-bearing premise

Theory-based value definitions can constrain interpretations and reduce spurious value attributions even when texts permit multiple plausible readings.

What would settle it

If an encoder model trained on the soft labels produces value predictions on held-out expert-annotated posts that show markedly lower alignment with theory or lose the observed relations between predicted values and uncertainty measures.

Figures

Figures reproduced from arXiv: 2606.11018 by Maksim Rudnev, Maria Milkova.

Figure 1
Figure 1. Figure 1: Distribution of expert votes in Dataset-1 [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Higher-order structure of Schwartz’s basic [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Over- and under-attribution rates across val [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Domain-aware Jaccard similarity across sev [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Contexts associated with over- and under [PITH_FULL_IMAGE:figures/full_fig_p007_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Cumulative distribution functions. present in expert judgments. Performance on the held-out test dataset is reported in Appendix H.2. 4.8 Discussion Our findings contribute to the growing literature on human value detection by shifting attention from classification performance alone to the problem of value measurement. Previous research has shown that values can be detected in text and that LLMs can be use… view at source ↗
Figure 7
Figure 7. Figure 7: Changes in Precision, Recall, F1, and over-attribution rates across stages of prompt refinement. [PITH_FULL_IMAGE:figures/full_fig_p014_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Distribution of expert votes across values in Dataset-2. Each value was independently evaluated on a [PITH_FULL_IMAGE:figures/full_fig_p016_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Contexts associated with over- and under- attributed values for Dataset-2. [PITH_FULL_IMAGE:figures/full_fig_p017_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Targeted expert verification workflow. G Train dataset (N=20,000) 18 [PITH_FULL_IMAGE:figures/full_fig_p018_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Distribution of positive votes across values. [PITH_FULL_IMAGE:figures/full_fig_p019_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Annotation differences on train dataset: Gemini 2.5 (bias-calibrated prompt) vs GPT-4 (baseline prompt) [PITH_FULL_IMAGE:figures/full_fig_p020_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: Cumulative distribution functions. 21 [PITH_FULL_IMAGE:figures/full_fig_p021_13.png] view at source ↗
read the original 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. Finally, 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

0 major / 2 minor

Summary. The paper claims that iterative calibration of LLM prompts against expert annotations for Schwartz's basic human values in non-English social media texts reduces misattributions and improves alignment; targeted verification rules derived from error patterns further support annotation; and LLM annotations can be transferred to an encoder via soft-label training while retaining theory-grounded value interpretations and uncertainty signals, with theory definitions helping constrain multiple plausible readings.

Significance. If the transfer and calibration results hold with the reported evaluations of structural alignment, error structure, confidence-ambiguity relations, and stability, the work offers a scalable, theory-constrained pipeline for measuring subjective constructs in large text corpora. The use of soft labels to preserve uncertainty and the explicit focus on reducing spurious attributions via theory are strengths that could advance computational social science methods beyond standard precision/recall metrics.

minor comments (2)
  1. [Abstract] Abstract: the claim of improved alignment and retained interpretations would be strengthened by previewing at least one key quantitative result (e.g., F1 improvement or alignment metric) rather than stating only that calibration 'reduces misattributions.'
  2. [Methods] The manuscript should clarify in the methods section how 'soft-label training' is implemented (e.g., exact loss function or temperature scaling) to allow replication of the uncertainty-preserving transfer.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for the positive assessment of the work, the accurate summary of our contributions, and the recommendation for minor revision. No specific major comments were raised in the report.

Circularity Check

0 steps flagged

No significant circularity; derivation relies on external expert annotations and standard transfer techniques

full rationale

The paper's pipeline—LLM annotation calibrated iteratively against expert annotations per Schwartz's external theory, derivation of verification rules from error patterns, and soft-label transfer to an encoder—contains no self-definitional steps, fitted inputs renamed as predictions, or load-bearing self-citations. All evaluations (structural alignment, error structure, confidence-ambiguity) are benchmarked against independent expert labels rather than internal fits. The abstract and description indicate a self-contained empirical workflow without equations or ansatzes that reduce to their own inputs by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Only the abstract is available; no explicit free parameters, axioms, or invented entities can be extracted beyond the background use of Schwartz's theory as a domain assumption.

pith-pipeline@v0.9.1-grok · 5702 in / 991 out tokens · 40580 ms · 2026-06-27T13:11:49.887556+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

39 extracted references · 2 canonical work pages

  1. [1]

    Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , pages =

    Identifying the Human Values behind Arguments , author =. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , pages =. 2022 , publisher =

    2022

  2. [2]

    Mirzakhmedova, Nailia and Kiesel, Johannes and Alshomary, Milad and Heinrich, Maximilian and Handke, Nicolas and Cai, Xiaoni and Barriere, Valentin and Dastgheib, Doratossadat and Ghahroodi, Omid and Sadraei, Mohammad Ali and Asgari, Ehsaneddin and Kawaletz, Lea and Wachsmuth, Henning and Stein, Benno , booktitle =. The. 2024 , publisher =

    2024

  3. [3]

    Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , pages =

    Mining the Uncertainty Patterns of Humans and Models in the Annotation of Moral Foundations and Human Values , author =. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , pages =. 2025 , publisher =

    2025

  4. [4]

    2025 , eprint =

    Measuring Value Expressions in Social Media Posts , author =. 2025 , eprint =

    2025

  5. [5]

    New Media & Society , year =

    The Expression of Values on Social Media: An Analytical Framework , author =. New Media & Society , year =

  6. [6]

    Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers) , pages =

    The Perspectivist Paradigm Shift: Assumptions and Challenges of Capturing Human Labels , author =. Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers) , pages =. 2024 , publisher =

    2024

  7. [7]

    Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing , pages =

    The ``Problem'' of Human Label Variation: On Ground Truth in Data, Modeling and Evaluation , author =. Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing , pages =. 2022 , publisher =

    2022

  8. [8]

    Transactions of the Association for Computational Linguistics , volume =

    Dealing with Disagreements: Looking Beyond the Majority Vote in Subjective Annotations , author =. Transactions of the Association for Computational Linguistics , volume =. 2022 , doi =

    2022

  9. [9]

    Feder Cooper, Angelina Wang, Chad Atalla, Solon Barocas, Su Lin Blodgett, Alexandra Chouldechova, Emily Corvi, P

    Wallach, Hanna and Cooper, A. Feder and Hanna, Alex and Jacobs, Abigail Z. and Licht, Henning and Narayanan, Arvind and Kleinberg, Jon and Tisue, Seth and others , year =. Position: Evaluating Generative. 2502.00561 , archivePrefix =

    work page arXiv

  10. [10]

    , journal =

    Halterman, Andrew and Keith, Katherine A. , journal =. Codebook. 2025 , doi =

    2025

  11. [11]

    Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing , pages =

    Large Language Models for Data Annotation and Synthesis: A Survey , author =. Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing , pages =. 2024 , publisher =

    2024

  12. [12]

    The Alternative Annotator Test for

    Calderon, Nitay and Reichart, Roi and Dror, Rotem , booktitle =. The Alternative Annotator Test for. 2025 , publisher =

    2025

  13. [13]

    PNAS Nexus , volume =

    Are Chatbots Reliable Text Annotators? Sometimes , author =. PNAS Nexus , volume =. 2025 , doi =

    2025

  14. [14]

    Which Demographics do

    Sch. Which Demographics do. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , year =

  15. [15]

    and Driggers, Hannah , journal =

    Vera, Sergio V. and Driggers, Hannah , journal =. 2025 , doi =

    2025

  16. [16]

    Personas with Attitudes: Controlling

    Fr. Personas with Attitudes: Controlling. Proceedings of the 9th Workshop on Online Abuse and Harms , pages =. 2025 , publisher =

    2025

  17. [17]

    Arbiters of Ambivalence: Challenges of Using

    Radharapu, Bhaktipriya and Revel, Manon and Ung, Megan and Ruder, Sebastian and Williams, Adina , booktitle =. Arbiters of Ambivalence: Challenges of Using. 2025 , publisher =

    2025

  18. [18]

    Proceedings of the Interdisciplinary Workshop on Observations of Misunderstood, Misguided and Malicious Use of Language Models , pages =

    Bias in, Bias out: Annotation Bias in Multilingual Large Language Models , author =. Proceedings of the Interdisciplinary Workshop on Observations of Misunderstood, Misguided and Malicious Use of Language Models , pages =. 2025 , publisher =

    2025

  19. [19]

    Keeping Humans in the Loop: Human-Centered Automated Annotation with Generative

    Pangakis, Nicholas and Wolken, Samuel , booktitle =. Keeping Humans in the Loop: Human-Centered Automated Annotation with Generative. 2025 , url =

    2025

  20. [20]

    Wang, Xinru and Kim, Hannah and Rahman, Sajjadur and Mitra, Kushan and Miao, Zhijie , booktitle =. Human-. 2024 , publisher =

    2024

  21. [21]

    2024 , publisher =

    Kim, Hannah and Mitra, Kushan and Chen, Rafael Li and Rahman, Sajjadur and Zhang, Dan , booktitle =. 2024 , publisher =

    2024

  22. [22]

    Just Put a Human in the Loop? Investigating

    Schroeder, Hope and Roy, Deb and Kabbara, Jad , booktitle =. Just Put a Human in the Loop? Investigating. 2025 , publisher =

    2025

  23. [23]

    Can Unconfident

    Gligori. Can Unconfident. Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers) , pages =. 2025 , publisher =

    2025

  24. [24]

    Enhancing Text Classification through

    Rouzegar, Hamidreza and Makrehchi, Masoud , booktitle =. Enhancing Text Classification through. 2024 , publisher =

    2024

  25. [25]

    Data Mining and Knowledge Discovery , volume =

    Evaluation Measures for Hierarchical Classification: A Unified View and Novel Approaches , author =. Data Mining and Knowledge Discovery , volume =. 2015 , doi =

    2015

  26. [26]

    Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , pages =

    Evaluating Extreme Hierarchical Multi-label Classification , author =. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , pages =. 2022 , publisher =

    2022

  27. [27]

    Proceedings of the 28th Conference on Computational Natural Language Learning , year =

    Revisiting Hierarchical Text Classification: Inference and Metrics , author =. Proceedings of the 28th Conference on Computational Natural Language Learning , year =

  28. [28]

    Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing , year =

    A Unified View of Evaluation Metrics for Structured Prediction , author =. Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing , year =

    2023

  29. [29]

    Advances in Experimental Social Psychology , volume =

    Universals in the Content and Structure of Values: Theoretical Advances and Empirical Tests in 20 Countries , author =. Advances in Experimental Social Psychology , volume =. 1992 , publisher =

    1992

  30. [30]

    PNAS Nexus , volume =

    Cultural Bias and Cultural Alignment of Large Language Models , author =. PNAS Nexus , volume =. 2024 , doi =

    2024

  31. [31]

    Proceedings of the 13th Brazilian Conference on Intelligent Systems (BRACIS) , year=

    Beyond Single Models: Leveraging LLM Ensembles for Human Value Detection in Text , author=. Proceedings of the 13th Brazilian Conference on Intelligent Systems (BRACIS) , year=

  32. [32]

    arXiv preprint arXiv:2501.11770 , year=

    The value of nothing: Multimodal extraction of human values expressed by TikTok influencers , author=. arXiv preprint arXiv:2501.11770 , year=

    work page arXiv

  33. [33]

    Findings of the Association for Computational Linguistics: EMNLP 2024 , pages=

    Exploring Multilingual Concepts of Human Values in Large Language Models: Is Value Alignment Consistent, Transferable and Controllable across Languages? , author=. Findings of the Association for Computational Linguistics: EMNLP 2024 , pages=. 2024 , url=

    2024

  34. [34]

    Hierocles of Alexandria at Touch

    Legkas, Sotirios and Christodoulou, Christina and Zidianakis, Matthaios and Koutrintzes, Dimitrios and Dagioglou, Maria and Petasis, Georgios , booktitle=. Hierocles of Alexandria at Touch. 2024 , url =

    2024

  35. [35]

    arXiv e-prints , pages=

    Value Lens: Using Large Language Models to Understand Human Values , author=. arXiv e-prints , pages=. 2025 , doi=

    2025

  36. [36]

    Proceedings of the 1st Workshop on Benchmarking: Past, Present and Future , year=

    We Need to Consider Disagreement in Evaluation , author=. Proceedings of the 1st Workshop on Benchmarking: Past, Present and Future , year=

  37. [37]

    Will Annotators Disagree? Identifying Subjectivity in Value-Laden Arguments

    Homayounirad, Amir and Liscio, Enrico and Wang, Tong and Jonker, Catholijn M and Siebert, Luciano Cavalcante. Will Annotators Disagree? Identifying Subjectivity in Value-Laden Arguments. Findings of the Association for Computational Linguistics: EMNLP 2025. 2025

    2025

  38. [38]

    Comparing Large Language Models and Human Annotators in Latent Content Analysis of Sentiment, Political Leaning, Emotional Intensity and Sarcasm , journal =

    Boji. Comparing Large Language Models and Human Annotators in Latent Content Analysis of Sentiment, Political Leaning, Emotional Intensity and Sarcasm , journal =. 2025 , pages =

    2025

  39. [39]

    Journal of Personality and Social Psychology , volume=

    Refining the theory of basic individual values , author=. Journal of Personality and Social Psychology , volume=. 2012 , publisher=

    2012