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Document Topic Alignment Metrics for Evaluating Topic Models of Short-Text Public Health Communications on Social Media — arXiv:2609.14256

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.14256
License (as archived): CC0-1.0
Archived: 2026-10-15T23:15:04.177Z
SHA-256 of the archived text: ff610d3b171acad365d76e42e42050caaa5521543b542f5bebeffbf1eed03457
Stable URL of this page: https://metraipolis.com/documento/dc_290ee994ecae

How to cite

Document Topic Alignment Metrics for Evaluating Topic Models of Short-Text Public Health Communications on Social Media — arXiv:2609.14256 — arXiv (en), https://arxiv.org/abs/2609.14256 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_290ee994ecae (Metraipolis archive dc_290ee994ecae, sha256 ff610d3b171acad365d76e42e42050caaa5521543b542f5bebeffbf1eed03457)

BibTeX

@misc{dc290ee994ecae,
  title = {Document Topic Alignment Metrics for Evaluating Topic Models of Short-Text Public Health Communications on Social Media — arXiv:2609.14256},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.14256},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_290ee994ecae; sha256 ff610d3b171acad365d76e42e42050caaa5521543b542f5bebeffbf1eed03457},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.14256. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Topic models are widely used to analyze public health-related social media short texts, yet their evaluation remains dominated by metrics that focus entirely on generated topics alone. There is a lack of metrics that quantitatively assess whether assigned topics meaningfully represent the corresponding short-text posts. We propose Document-Topic Alignment metrics (DoTA), an assignment-aware evaluation framework comprising metrics that measure semantic alignment between documents (posts) and their assigned topics. We also introduce margin-based and discriminative variants that capture topic assignment confidence and distinguishability. We evaluate DoTA across five topic models on three public health-related social media datasets from X and compare DoTA metrics with conventional topic-based metrics. Results show that DoTA provides complementary evaluation cues and aligns meaningfully with human evaluations. These findings establish the need for assignment-aware evaluation and demonstrate that the addition of DoTA enables a more comprehensive and practically meaningful evaluation for assessing short-text topic modeling performance.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-15 in cs.CL).
Categorie arXiv: Computation and Language (cs.CL) ; Social and Information Networks (cs.SI).
Pagina del lavoro: https://arxiv.org/abs/2609.14256 — 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 2026-10-15T22:51:09.871Z dalla pagina https://arxiv.org/catchup/cs.CL/2026-09-15?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).