新型コロナウイルス感染症拡大下における災害時避難への社会的関心の傾向―テキストマイニングによるWeb記事分析を通じて―
論文ID: SRA-0359
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論文ID: SRA-0359
The authors employed a text mining method based on published web articles to analyze how social interest in disaster evacuation and sheltering with COVID-19 changed over time. To understand social interest, the authors divided the number of COVID-19 positive cases in Japan into five phases. The results revealed a vague concern about the need for measures taken by local governments in Phase I. Furthermore, there were descriptions of actual countermeasures and training based on the heavy rain in July 2020, typhoon No. 9 (Maysak), and typhoon No. 10 (Haishen) in Phase III. Finally, in Phase V, it was possible to grasp how the social interest shifted to specific content.
