用Twitter数据分析COVID-19的情绪影响:意大利B-VAR分析的教训
Sabina De Rosis1, Milena Lopreite2, Michelangelo Puliga1
1Management and Healthcare Laboratory, Institute of Management and Department EMbeDS, Sant'Anna School of Advanced Studies of Pisa, Pisa, Italy.
概括
医生 医生 医生 医生
科学领域:
- 社交媒体分析
- 流行病学研究是流行病学研究.
- 公共卫生沟通公共卫生沟通
背景情况:
- 随着COVID-19大流行,通信发生了重大变化,社交媒体越来越多地用于表达公众情绪.
- 了解利益相关者的情绪对于管理公共卫生危机至关重要.
研究的目的:
- 分析意大利COVID-19大流行初期不同利益相关者群体在Twitter上表达的情绪.
- 调查意大利各地区记者,公众,医生和政治家之间的情感关系,按大流行影响分组.
主要方法:
- 创建一个对Twitter用户情绪的原始数据库.
- 对四个利益相关者群体 (记者,普通人,医生,政治家) 的情绪指数计算.
- 应用贝叶斯矢量自回归 (B-VAR) 技术来分析情绪动态.
主要成果:
- 医生的情绪在疫情初期显著影响了公共话语,特别是在像伦巴第 (组2) 和大多数地区 (组0) 等受到严重影响的地区.
- 情感关系因利益相关者群体和区域COVID-19影响而有所不同.
结论:
- 在流行病期间,利用医生等关键利益相关者的影响力进行沟通策略,对于有效的区域缓解计划至关重要.
- 为了成功制,卫生政策干预应考虑利益相关者的角色和危机规模.
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