mlsanityData to 14 Oct 2026

A Platform for Investigating Public Health Content with Efficient Concern Classification

arXiv 2506.01308 ↗First posted 2 Jun 2025cs.CL cs.IR cs.LGcited by 0

Not on the map. Why?

Abstract (arXiv)

A recent rise in online content expressing concerns with public health initiatives has contributed to already stalled uptake of preemptive measures globally. Future public health efforts must attempt to understand such content, what concerns it may raise among readers, and how to effectively respond to it. To this end, we present ConcernScope, a platform that uses a teacher-student framework for knowledge transfer between large language models and light-weight classifiers to quickly and effectively identify the health concerns raised in a text corpus. The platform allows uploading massive files directly, automatically scraping specific URLs, and direct text editing. ConcernScope is built on top of a taxonomy of public health concerns. Intended for public health officials, we demonstrate several applications of this platform: guided data exploration to find useful examples of common concerns found in online community datasets, identification of trends in concerns through an example time series analysis of 186,000 samples, and finding trends in topic frequency before and after significant events.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 6 of its 21 references that are in the map. First posted on arXiv 2 Jun 2025.

Builds on

6 of 21 references.

References · 6

  1. 2015Distilling the Knowledge in a Neural Networkcited by 10,324
  2. 2024A Survey on Knowledge Distillation of Large Language Modelscited by 284
  3. 2023From Words to Watts: Benchmarking the Energy Costs of Large Language Model Inferencecited by 170
  4. 2023Recent Advances in Hierarchical Multi-label Text Classification: A Surveycited by 6
  5. 2023Interface Design for Crowdsourcing Hierarchical Multi-Label Text Annotationscited by 3
  6. 2024Tailoring Vaccine Messaging with Common-Ground Opinionscited by 2

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2025Bridging the Gap: Leveraging Retrieval-Augmented Generation to Better Understand Public Concerns about Vaccines
  2. 2025RephQA: Evaluating Readability of Large Language Models in Public Health Question Answering
  3. 2023BAND: Biomedical Alert News Dataset
  4. 2024Leveraging Prompt-Based Large Language Models: Predicting Pandemic Health Decisions and Outcomes Through Social Media Language
  5. 2026Healthier LLMs: Retrieval-Augmented Generation for Public Health Question Answering
  6. 2026EpiQAL: Benchmarking Large Language Models in Epidemiological Question Answering and Reasoning
  7. 2025Beyond True or False: Retrieval-Augmented Hierarchical Analysis of Nuanced Claims
  8. 2026EpiNarrate: Agentic Generation of Grounded Narratives from Epidemiological Scenario Projections