Crisis and Emergency Risk Communication (CERC) in Social Media: A Bibliometric Analysis (Preprint)
Bibliographic record
Abstract
Background: Crisis and emergency risk communication (CERC) has been proposed as a way for government and public health organizations to improve their ability to connect with the public via social media while also integrating risk communication and crisis communication.However, few studies have systematically analyzed how CERC can be integrated with social media.Objective: Exploring the application of CERC in social media, comparing the distribution of platforms, the types and forms of data, the primary methods used, and clearer understanding of the main contributions and developmental context of CERC.Methods: We used the Web of Science, Scopus, and PubMed as databases to conduct a retrospective review with "Crisis and Emergency Risk Communication" as the search term from 2005 to 2021.Then, CERC was quantitatively and qualitatively analyzed.In the first part, we consider quantitative analysis as the core and publications as the research object, comprehensively covering the publication distribution in terms of time, periodical, country, institution, author, and discipline.In the second part, we use qualitative analysis to explore the composition of research topics related to keywords and the temporal variation trend of research topics based on keywords.The third part focuses on exploring the combination of CERC and social media.Finally, we propose future research routes based on the above results.Results: Health Promotion Practice is the most productive journal with seven contributing publications.The USA has become the dominant player in this field, with a considerable advantage of 28 publications.Australia, China, and the UK are far behind USA's contribution, although they are the second highest contributors.Centers for Disease Control and Prevention is the most contributing organization with three publications.Only seven people published more than two articles at the author level, among whom Lu, J. is from China, while Reynolds, B. and the other five are all from the United States.The cluster results show that the researchers mainly focus on five categories of issues: prevention and control of infectious diseases, disaster planning of bioterrorism, social media's role in risk and crisis communication, medical intervention in risk perception, the role of Twitter in public health crises. Conclusions:We raise some future concerns based on the use case of CERC in social media.First, existing research has focused too much on Twitter and may have overlooked the role of Facebook.Second, existing studies have focused on only one side of the government (organization) or the public, and have not considered the effectiveness of government or organizational communication strategies as assessed by public response.Third, the methodologies for studying tweets must be updated.Fourth, the research on false information in the precrisis stage must improve.Finally, the information forms of crisis communication of governments or organizations must be enriched.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.036 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.040 | 0.081 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".