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Natural Language Processing Psychometrics — arXiv:2608.07316

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.07316
License (as archived): CC0-1.0
Archived: 2026-10-19T09:47:00.607Z
SHA-256 of the archived text: 7d904f5a0f46b013d42329e111e5e46219a5e7e9077f8774fdf3fc68cc3e8183
Stable URL of this page: https://metraipolis.com/documento/dc_7b69ecd99ca9

How to cite

Natural Language Processing Psychometrics — arXiv:2608.07316 — arXiv (en), https://arxiv.org/abs/2608.07316 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_7b69ecd99ca9 (Metraipolis archive dc_7b69ecd99ca9, sha256 7d904f5a0f46b013d42329e111e5e46219a5e7e9077f8774fdf3fc68cc3e8183)

BibTeX

@misc{dc7b69ecd99ca9,
  title = {Natural Language Processing Psychometrics — arXiv:2608.07316},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.07316},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_7b69ecd99ca9; sha256 7d904f5a0f46b013d42329e111e5e46219a5e7e9077f8774fdf3fc68cc3e8183},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.07316. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Natural Language Processing (NLP) models predicting mental health outcomes rarely specify what they measure: contextual knowledge, emotional content, or syntactic structure. NLP Psychometrics treats psychological prediction from text as a psychometric problem, linking scores to interpretable linguistic evidence and testing beyond the training text format. Nine LLMs, conditioned on controlled personas (cognitive digital shadows), completed psychometric questionnaires with textual explanations per item. We extracted emotional profiles and syntactic-semantic structure via textual forma mentis networks, combined with personality and sociodemographic variables in ablated random forest (RF) regressors, using SHAP to identify which features drove performance and in which direction. Full RF models explained up to 70.8% of variance in life satisfaction (SWLS), 55.7% in depression (PHQ-9), and, for DASS-21, 68.5% depression, 76.0% anxiety, 72.4% stress. Sociodemographics alone explained no meaningful variance in depression, anxiety, or stress, but did so for life satisfaction, where emotion features and income were the strongest predictors; neuroticism and network topology instead dominated depression and anxiety, reversing direction between them. Without retraining, RF models separated diaries from low- and high-score personas ($r$ up to 0.91) and, using only network/emotion features, classified clinical from control participants in real transcripts with up to 68% accuracy. These results show the promise and limits of synthetic data: LLM personas can expose model biases, recover patterns consistent with clinical rumination, and support psychometric prediction from human text without a matched questionnaire, but cannot substitute for human validation. NLP Psychometrics makes these distinctions explicit, measurable, and testable through interpretable AI and network/emotional features.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-08-10 in cs.CL).
Categorie arXiv: Computation and Language (cs.CL) ; Artificial Intelligence (cs.AI); Social and Information Networks (cs.SI).
Pagina del lavoro: https://arxiv.org/abs/2608.07316 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-19T09:46:37.244Z dalla pagina https://arxiv.org/catchup/cs.CL/2026-08-10?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).