Risk communication plays a critical role in facilitating effective interactions between governments and the public, promoting compliance with coordinated policies, and enhancing the prevention and control of public emergency events. However, risk communication effectiveness, particularly its risk reduction efficiency within the context of large-scale social media in the era of big data remains underexplored. This study introduces a novel framework that extends the extended parallel process model (EPPM) by incorporating three quantifiable parameters—empathy, self-efficacy, and response-efficacy. These parameters characterize key efficacy components and are jointly used to evaluate the real-time effectiveness of risk communication. To compute these parameters, a contextualized topic model (CTM) is employed to extract and cluster latent topics from large-scale social media and policy data. Time-series and simultaneous correlation analyses are applied to relate topics to emergency dynamics and communication components. The model is validated using real-world data derived from announcements and mainstream news sources. Results show that self-efficacy is highly responsive to changes in perceived emergency severity, while response efficacy tends to decline over time if the public emergency is not effectively controlled. Efficacy appraisal emerges as a medium-term to long-term, internally formed perception, less influenced by real-time communication. This framework offers a real-time, data-driven approach for assessing risk communication efficiency and adjusting public messaging strategies in response to evolving public emergencies, contributing to more adaptive and sustainable governance and fewer losses in digital era.
Wang et al. (Sun,) studied this question.
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