Previously submitted to: JMIR Formative Research (no longer under consideration since May 15, 2026)
Date Submitted: Aug 26, 2025
Open Peer Review Period: Dec 10, 2025 - Feb 4, 2026
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A novel infoveillance framework: Dynamic topic consistency between CDC communications and public responses during the COVID-19 pandemic
ABSTRACT
Background:
During the COVID-19 pandemic, the U.S. Centers for Disease Control and Prevention (CDC) actively used social media to disseminate information. However, traditional engagement metrics (eg, likes, shares, replies) do not capture whether public responses are thematically consistent with official communications, leaving a critical gap in evaluating the effectiveness of digital health messaging.
Objective:
This study aimed to develop and evaluate a novel infoveillance framework that quantifies the dynamic consistency between CDC COVID-19 communications and public responses on social media.
Methods:
We collected 17,524 CDC posts and 26,812 public replies from Twitter/X between May 14, 2020, and November 10, 2022. Using the Biterm Topic Model (BTM), we extracted latent topics from CDC communications and public responses. We then developed a topic consistency scoring system that combined semantic similarity (cosine similarity of topic-word distributions) with daily public topic prominence (weights of response topics per day). The resulting weighted similarity scores generated a time series to assess topic alignment dynamically.
Results:
BTM identified 9 CDC topics and 8 public response topics. Alignment varied across topics and over time. CDC topics related to epidemic outcomes (eg, hospitalization, death) and high-risk populations consistently showed high alignment with public responses, especially during Delta and Omicron variant phases. In contrast, topics on non-pharmaceutical interventions exhibited lower weighted similarity, indicating weaker public engagement despite moderate semantic similarity. The weighted similarity metric outperformed conventional semantic similarity measures by accounting for daily shifts in public attention.
Conclusions:
This study introduces a flexible and modular framework for public health infoveillance that quantifies dynamic alignment between official health communications and public discourse. Findings highlight which CDC messages were most and least aligned with public concerns, offering evidence to improve communication strategies during future health emergencies. The framework is generalizable and can integrate alternative topic models, similarity measures, and social media platforms.
Citation
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