地理学报 ›› 2020, Vol. 75 ›› Issue (11): 2490-2504.doi: 10.11821/dlxb202011016
王卷乐1,5(), 张敏1,2, 韩雪华1,2, 王晓洁1,3, 郑莉1,4
收稿日期:
2020-05-08
修回日期:
2020-10-12
出版日期:
2020-11-25
发布日期:
2021-01-25
作者简介:
王卷乐(1976-), 男, 河南洛阳人, 博士, 研究员, 主要从事资源环境科学数据集成与共享研究。E-mail: 基金资助:
WANG Juanle1,5(), ZHANG Min1,2, HAN Xuehua1,2, WANG Xiaojie1,3, ZHENG Li1,4
Received:
2020-05-08
Revised:
2020-10-12
Published:
2020-11-25
Online:
2021-01-25
Supported by:
摘要:
COVID-19疫情是全球面临的重大公共卫生危机。客观认识疫情期间的公众舆情响应和区域差异,对于提高重大公共卫生事件的政策调控和科学治理具有现实意义。本文以新浪微博为数据源,基于潜在狄利克雷分配主题模型和随机森林算法构建了主题抽取和分类模型,识别微博文本中的13个舆情话题,并从数量、空间、时间、内容等方面分析了2020年1月9日—3月10日在湖北省、京津冀、长三角、珠三角、成渝等城市群及沿边口岸等重点区域分布特点。结果表明:中国公众的响应总体是理性和积极的,但各舆情话题在区域内部的空间分布差异明显。各区域热点分布中,京津冀以首都北京为中心,长三角以上海为中心,辅以南京、杭州等热点,珠三角以广州、深圳为两核,湖北省以武汉为中心。建议应持续加强重点区域的疫情舆情关注和因地制宜的差异化精准响应。
王卷乐, 张敏, 韩雪华, 王晓洁, 郑莉. COVID-19疫情防控中的中国公众舆情时空演变特征[J]. 地理学报, 2020, 75(11): 2490-2504.
WANG Juanle, ZHANG Min, HAN Xuehua, WANG Xiaojie, ZHENG Li. Spatio-temporal evolution and regional differences of the public opinion on the prevention and control of COVID-19 epidemic in China[J]. Acta Geographica Sinica, 2020, 75(11): 2490-2504.
表1
区域人口与话题微博数量相关系数
区域 | 恐惧 担忧 | 质疑当地政府/媒体 | 谴责 恶习 | 客观 评论 | 科学 防疫 | 祈福 祝愿 | 倡导 救助 | 复工 意愿 | 就医 求助 | 物资 求助 | 呼吁海外重视疫情 | 关心全 球疫情 | 微博 总数 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
京津冀 | 0.839** | 0.800** | 0.849** | 0.826** | 0.833** | 0.866** | 0.888** | 0.801** | 0.782** | 0.843** | 0.869** | 0.812** | 0.838** |
长三角 | 0.938** | 0.848** | 0.948** | 0.951** | 0.946** | 0.841** | 0.379 | 0.862** | 0.898** | 0.740** | 0.896** | 0.945** | 0.945** |
珠三角 | 0.872** | 0.852** | 0.879** | 0.881** | 0.875** | 0.919** | 0.312 | 0.830** | 0.562 | 0.314 | 0.829** | 0.853** | 0.884** |
成渝 | 0.889** | 0.607** | 0.878** | 0.849** | 0.848** | 0.833** | 0.605** | 0.882** | 0.542** | 0.941** | 0.847** | 0.710** | 0.853** |
湖北 | 0.819** | 0.797** | 0.796** | 0.797** | 0.832** | 0.808** | 0.814** | 0.819** | 0.743** | 0.775** | 0.787** | 0.811** | 0.807** |
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