Background: Health agencies have been widely adopting social media to disseminate important information, educate the public on emerging health issues, and understand public opinions. The Centers for Disease Control and Prevention (CDC) widely used social media platforms during the COVID-19 pandemic to communicate with the public and mitigate the disease in the United States. It is crucial to understand the relationships between the CDC’s social media communications and the actual epidemic metrics to improve public health agencies’ communication strategies during health emergencies. Objective: This study aimed to identify key topics in tweets posted by the CDC during the pandemic, investigate the temporal dynamics between these key topics and the actual COVID-19 epidemic measures, and make recommendations for the CDC’s digital health communication strategies for future health emergencies. Methods: Two types of data were collected: (1) a total of 17,524 COVID-19–related English tweets posted by the CDC between December 7, 2019, and January 15, 2022, and (2) COVID-19 epidemic measures in the United States from the public GitHub repository of Johns Hopkins University from January 2020 to July 2022. Latent Dirichlet allocation topic modeling was applied to identify key topics from all COVID-19–related tweets posted by the CDC, and the final topics were determined by domain experts. Various multivariate time series analysis techniques were applied between each of the identified key topics and actual COVID-19 epidemic measures to quantify the dynamic associations between these 2 types of time series data. Results: Four major topics from the CDC’s COVID-19 tweets were identified: (1) information on the prevention of health outcomes of COVID-19; (2) pediatric intervention and family safety; (3) updates of the epidemic situation of COVID-19; and (4) research and community engagement to curb COVID-19. Multivariate analyses showed that there were significant variabilities of progression between the CDC’s topics and the actual COVID-19 epidemic measures. Some CDC topics showed substantial associations with the COVID-19 measures over different time spans throughout the pandemic, expressing similar temporal dynamics between these 2 types of time series data. Conclusions: Our study is the first to comprehensively investigate the dynamic associations between topics discussed by the CDC on Twitter and the COVID-19 epidemic measures in the United States. We identified 4 major topic themes via topic modeling and explored how each of these topics was associated with each major epidemic measure by performing various multivariate time series analyses. We recommend that it is critical for public health agencies, such as the CDC, to update and disseminate timely and accurate information to the public and align major topics with key epidemic measures over time. We suggest that social media can help public health agencies to inform the public on health emergencies and to mitigate them effectively.

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 Health agencies have been widely adopting social media to disseminate important information, educate the public on emerging health issues, and understand public opinions. The Centers for Disease Control and Prevention (CDC) widely used social media platforms during the COVID-19 pandemic to communicate with the public and mitigate the disease in the United States. It is crucial to understand the relationships between the CDC’s social media communications and the actual epidemic metrics to improve public health agencies’ communication strategies during health emergencies. OBJECTIVE This study aimed to identify key topics in tweets posted by the CDC during the pandemic, investigate the temporal dynamics between these key topics and the actual COVID-19 epidemic measures, and make recommendations for the CDC’s digital health communication strategies for future health emergencies. METHODS Two types of data were collected: (1) a total of 17,524 COVID-19–related English tweets posted by the CDC between December 7, 2019, and January 15, 2022, and (2) COVID-19 epidemic measures in the United States from the public GitHub repository of Johns Hopkins University from January 2020 to July 2022. Latent Dirichlet allocation topic modeling was applied to identify key topics from all COVID-19–related tweets posted by the CDC, and the final topics were determined by domain experts. Various multivariate time series analysis techniques were applied between each of the identified key topics and actual COVID-19 epidemic measures to quantify the dynamic associations between these 2 types of time series data. RESULTS Four major topics from the CDC’s COVID-19 tweets were identified: (1) information on the prevention of health outcomes of COVID-19; (2) pediatric intervention and family safety; (3) updates of the epidemic situation of COVID-19; and (4) research and community engagement to curb COVID-19. Multivariate analyses showed that there were significant variabilities of progression between the CDC’s topics and the actual COVID-19 epidemic measures. Some CDC topics showed substantial associations with the COVID-19 measures over different time spans throughout the pandemic, expressing similar temporal dynamics between these 2 types of time series data. CONCLUSIONS Our study is the first to comprehensively investigate the dynamic associations between topics discussed by the CDC on Twitter and the COVID-19 epidemic measures in the United States. We identified 4 major topic themes via topic modeling and explored how each of these topics was associated with each major epidemic measure by performing various multivariate time series analyses. We recommend that it is critical for public health agencies, such as the CDC, to update and disseminate timely and accurate information to the public and align major topics with key epidemic measures over time. We suggest that social media can help public health agencies to inform the public on health emergencies and to mitigate them effectively.

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.

BACKGROUND Public health agencies increasingly rely on social media platforms such as X (formerly Twitter) to monitor public responses to health communications and to support timely, data-driven decision-making during health crises such as the COVID-19 pandemic. In particular, public replies to official communications from agencies such as the Centers for Disease Control and Prevention (CDC) provide valuable insights into population-level perceptions, concerns, and engagement with health policies. However, effectively analyzing these large-scale short-text responses remains challenging due to their brevity, informality, and linguistic variability. Topic modeling offers a scalable approach to identifying thematic patterns in such data, but its performance is often limited when applied to short, noisy social media text, resulting in low-quality and difficult-to-interpret topics. Although recent advances in large language models (LLMs) provide new opportunities to enhance text representation, their potential to improve topic modeling for public health–oriented social media analysis remains underexplored. OBJECTIVE This study aims to enhance public health surveillance by improving the analysis of public responses to official health communications on social media. To achieve this, we develop and evaluate TM-Rephrase, a model-agnostic framework that leverages LLM–based rephrasing to improve the quality, interpretability, and semantic relevance of topics derived by topic models for public health-related short texts on social media. METHODS We analyzed 25,027 public replies to official CDC posts on X collected between May 2020 and November 2022 to examine public responses to health communications. We applied a LLM–based rephrasing framework (TM-Rephrase) to transform informal short texts into more standardized and context-enriched representations using general and colloquial-to-formal schemes. Both original and rephrased texts were analyzed across multiple topic models, and topic quality was evaluated using coherence, uniqueness, redundancy, and diversity metrics, along with qualitative assessment of interpretability and post-topic semantic alignment. RESULTS TM-Rephrase consistently improved topic quality across models and evaluation metrics. For LDA, topic coherence increased from Cv =0.3094 (no rephrasing) to Cv =0.5004 with colloquial-to-formal rephrasing (>60% relative improvement). For BERTopic, coherence improved from Cv=0.4078 to Cv =0.4734. Diversity-related metrics also improved, with TSCTM achieving TU=1.0, TD=1.0, and TR=0 under rephrased conditions, indicating fully distinct topics. These improvements were robust across multiple LLMs (Gemini, GPT-4o-mini, and Mistral-7B). Qualitative results further showed that rephrased texts produced more interpretable and semantically coherent topics (representative keywords), enabling clearer identification of public concerns and responses to health communications, including themes related to vaccination attitudes, perceived risks, and public health measures. This improvement facilitates more reliable characterization of public discourse, which is critical for supporting social media–based public health surveillance and informing communication strategies. CONCLUSIONS TM-Rephrase enhances the reliability and interpretability of topic modeling for short, noisy social media data in public health contexts, enabling more actionable insights for public health infoveillance. By improving semantic clarity without modifying underlying models, this framework provides a scalable and practical technique for monitoring public discourse and supporting evidence-based public health communication and decision-making.