Evidence map›Paper›PMID 40453490›Full record

ArticleFrontiers in public health2025

Analyzing the spatial-temporal dynamics of disaster risk based on social media data: a case study of Weibo during the Typhoon Yagi period.

Xingqi Zou, Jiaxin Lv

Abstract read
In one paragraph

Article in Frontiers in public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Xingqi ZouFaculty of Management and Economics, Kunming University of Science and Technology, Kunming, China.
Jiaxin LvFaculty of Management and Economics, Kunming University of Science and Technology, Kunming, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This paper investigates the spatial-temporal dynamics of disaster risk diffusion and the dissemination of emergency knowledge during Typhoon Yagi, one of the strongest autumn typhoons to hit China since 1949. Employing the spatial-temporal dynamics approach, the study utilizes social media data from Sina Weibo, collected through Python crawling, to analyze the diffusion process of typhoon disaster risks and the mechanism of emergency knowledge dissemination. The research examines the spatial-temporal characteristics of disaster risk diffusion and emergency knowledge dissemination, their interrelationships, and the influence of social background and geographical environment. The findings reveal that the public's discussion on disaster risk and emergency knowledge changes over time, with distinct patterns observed during the typhoon warning, occurrence, and recovery periods. Spatially, Guangdong and Hainan provinces show the highest levels of discussion, aligning with the typhoon's landfall locations. The study underscores the co-evolutionary nature of disaster risk diffusion and emergency knowledge dissemination, whereby the dissemination of emergency knowledge is concomitant with the diffusion of typhoon risk. This research provides a theoretical foundation for the field of disaster risk management and emergency knowledge dissemination, offering practical references for future responses to natural risks.

Indexed as

Cyclonic StormsDisastersInformation DisseminationSocial MediaChinaHumansSpatio-Temporal Analysisdisaster risk diffusionemergency knowledge disseminationgrounded theoryspatial–temporal dynamicsTyphoon Yagi

Identifiers

PMID40453490
PMCPMC12122763

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.