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Evaluation of Emergency Social Media Language Efficiency Based on Persuasion Theory and Data Envelopment Analysis: A Case Study of the 2025 Beijing Extreme Rainfall Event

Year: 2025

Type: article

Abstract: In the context of urban extreme weather events, the efficacy of the “emergency language” employed by governments and public institutions on social media in effectively reaching and guiding the public in a timely manner necessitates a quantifiable evaluation framework. An indicator system was constructed on the basis of Hovland’s persuasion theory. This system comprised five input characteristics (word count/structural clarity, first/second-person perspective, emotional appeal, evidence and framing, and media format) along with three output indicators (reposts, comments, and likes). A data envelopment analysis (DEA) model that is oriented towards output was employed, with disseminators being categorized into four distinct decision-making units: central mainstream media, other government media, local government media, and other media. It is imperative to note that the outputs were subjected to a process of normalization through the implementation of a scale factor. The data were sourced from the Weibo platform within the specified time window, which was from 10:00 on 24 July 2025, to 12:00 on 19 August 2025, with a sample size of 744. The findings revealed substantial disparities in technical efficiency across different disseminator types. A subset of local government media demonstrated a technical efficiency ≈ 1.00 yet low scale efficiency. Posts exhibiting clear structures, actionable points, and accompanying images or videos achieved higher cross-efficiency scores. It is therefore evident that the proposed DEA model provides a benchmark for maximizing dissemination effectiveness under given information characteristics. It is recommended that posting frequencies be maintained at consistent intervals during periods of heightened activity, that a template structure be adopted in accordance with the “fact–action–assistance channel” model, and that the proportion of rich media content be augmented.

Source: Applied Sciences

DOI: 10.3390/app152111435

Authors: Jingqi Gao, Yutong Zu, Shigen Fu, Jianwu Chen, Shufang Li, Hezhuang Lou

Institutions: China University of Geosciences (Beijing), China Academy of Safety Sciences and Technology, University of Science and Technology Beijing, Beijing Drainage Group (China)

Language: English

Topic: Public Relations and Crisis Communication

Subfield: Communication

Field: Social Sciences

Domain: Social Sciences

SDG: Sustainable cities and communities

Open Access status: gold

Estimated APC paid: $2,896