Hybrid quantum-classical learning pipelines combine conventional accelerators, quantum runtimes, and quantum processing units (QPUs), creating scheduling, memory, isolation, encoding, and deployment challenges that are not captured by application-level quantum machine learning surveys alone. This paper presents a systematic review of runtime and systems mechanisms for hybrid quantum-classical workloads, with medical imaging used as a translation lens rather than as an exclusive inclusion boundary. Following a PRISMA-aligned review process, we screened 364 records and synthesized 40 studies published between 2020 and 2025. Each study was coded by systems layer, application grounding, noisy-label relevance, and evaluation maturity. The coding shows that the corpus combines direct medical evidence with broader transferable systems evidence: 8 studies directly evaluated medical data, 12 were medically motivated, and 20 were generic systems studies. Across the corpus, the strongest support concerns hybrid orchestration, qubit/resource allocation, classical–quantum data movement, and container-based reproducibility, whereas evidence remains limited for realistic clinical operation, end-to-end remote-QPU workflows, multi-tenant isolation, and noisy-label retraining loops. We contribute an evidence map, a direct/indirect/interpretive evidence distinction, and cross-layer design guidelines for future hybrid quantum-classical imaging pipelines in regulated settings.

Social media has become a critical channel for public health communication during the COVID-19 pandemic, yet how official health messaging aligns with broader public discourse remains insufficiently understood. This study develops an end-to-end infoveillance framework to examine the dynamic relationship between Centers for Disease Control and Prevention (CDC) communications and general public discourse on social media. We analyzed 17,524 CDC tweets and 67,895 public discourse tweets. Biterm Topic Model (BTM) was used to extract topics from each corpus, and a novel topic consistency scoring system integrating cosine similarity with daily public topic prominence was developed to quantify temporal alignment between official health communication and public discourse. Two complementary sentiment measures were incorporated: expected sentiment (average emotional tone) and net sentiment (overall emotional intensity). Temporal relationships were examined using autoregressive integrated moving average with exogenous variables (ARIMAX) models. Results show that topic alignment increased over time across CDC topics, while expected sentiment remained consistently negative. Higher alignment was associated with immediate and delayed changes in expected sentiment and stronger emotional intensity in net sentiment based on ARIMAX results. These findings suggest that topic alignment reflects public attention rather than agreement with official communications and is associated with more negative emotional responses. This framework provides a scalable, generalizable approach to investigating and evaluating public engagement with official health communication.

Background: Public health agencies increasingly use social media platforms such as Twitter (subsequently rebranded X, X Corp) to monitor public responses to official health communications and support timely decision-making during health crises. Public replies to communications from official agencies provide valuable insights into population-level discourse and engagement with health policies. However, analyzing large-scale short-text responses remains challenging because social media replies are often brief, informal, ambiguous, and linguistically fragmented. Topic modeling offers a scalable approach for identifying thematic patterns in such data, but its performance is often limited when applied to social media short texts. Recent advances in large language models (LLMs) provide new opportunities to improve text normalization before topic modeling, but their value and limitations for public health infoveillance remain underexplored. Objective: This study develops and evaluates TM-Rephrase, a model-agnostic LLM-based rephrasing framework designed to improve the performance of topic models for short public health social media texts while ensuring rephrasing preserves the original semantic meaning, stance or intent, and tone. Methods: We analyzed 25,027 public replies to official Centers for Disease Control and Prevention tweets on Twitter collected between May 2020 and November 2022. TM-Rephrase transformed informal and noisy short texts into more standardized expressions using 2 prompt-guided schemes: general rephrasing and colloquial-to-formal rephrasing. Original and rephrased texts were analyzed using multiple topic models, with rephrasing generated by Gemini 2.5 Flash, GPT‑4o mini, and Mistral-7B-Instruct. Topic quality was evaluated using coherence, uniqueness, redundancy, and diversity. We also conducted an expert semantic-fidelity validation study in which 4 expert raters evaluated whether rephrased texts preserved the original meaning, stance or intent, and tone. Results: TM-Rephrase generally improved topic quality across models, rephrasing schemes, and LLM backbones. For latent Dirichlet allocation, C v coherence increased from 0.3094 without rephrasing to 0.5004 with colloquial-to-formal rephrasing. For BERTopic (BERT-based topic modeling), C v coherence improved from 0.4078 to 0.4734. Diversity-related metrics also improved in most rephrasing settings. Additional checks using C N P M I , C U C I , and U M A S S generally supported the main improvement pattern. Sensitivity analysis across different topic numbers further suggested that the coherence gains were not limited to the main setting. Expert validation showed favorable semantic preservation overall, with a mean rating of 3.90 (SD 1.01) and median of 4.00 (IQR 3.00-5.00) of 5.00, and acceptable interrater reliability, intraclass correlation coefficient =0.736. General rephrasing received stronger semantic-fidelity ratings than colloquial-to-formal rephrasing, suggesting that stronger formalization may improve topic readability while introducing greater risk of changes to the original meaning, stance or intent, and tone. Conclusions: TM-Rephrase can enhance the coherence, distinctiveness, and interpretability of topic modeling outputs for short, noisy public health social media data. However, it is a text-normalization preprocessing strategy for topic enhancement rather than a fully neutral replacement for original public discourse.

Domestic Violence (DV) is a pervasive public health problem characterized by patterns of coercive and abusive behavior within intimate relationships. With the rise of social media as a key outlet for DV victims to disclose their experiences, online self-disclosure has emerged as a critical yet underexplored avenue for support-seeking. In addition, existing research lacks a comprehensive and nuanced understanding of DV self-disclosure, support provisions, and their connections. To address these gaps, this study proposes a novel computational framework for modeling DV support-seeking behavior alongside community support mechanisms. The framework consists of four key components: self-disclosure detection, post clustering, topic summarization, and support extraction and mapping. We implement and evaluate the framework with data collected from relevant social media communities. Our findings not only advance existing knowledge on DV self-disclosure and online support provisions but also enable victim-centered digital interventions.

Social media platforms such as Twitter (now X) provide rich data for analyzing public discourse, especially during crises such as the COVID-19 pandemic. However, the brevity, informality, and noise of social media short texts often hinder the effectiveness of traditional topic modeling, producing incoherent or redundant topics that are often difficult to interpret. To address these challenges, we have developed \emph{TM-Rephrase}, a model-agnostic framework that leverages large language models (LLMs) to rephrase raw tweets into more standardized and formal language prior to topic modeling. Using a dataset of 25,027 COVID-19-related Twitter posts, we investigate the effects of two rephrasing strategies, general- and colloquial-to-formal-rephrasing, on multiple topic modeling methods. Results demonstrate that \emph{TM-Rephrase} improves three metrics measuring topic modeling performance (i.e., topic coherence, topic uniqueness, and topic diversity) while reducing topic redundancy of most topic modeling algorithms, with the colloquial-to-formal strategy yielding the greatest performance gains and especially for the Latent Dirichlet Allocation (LDA) algorithm. This study contributes to a model-agnostic approach to enhancing topic modeling in public health related social media analysis, with broad implications for improved understanding of public discourse in health crisis as well as other important domains.