A new data integration framework for Covid-19 social media information.
A new data integration framework for Covid-19 social media information.
复制标题
DOI：
10.1038/s41598-023-33141-y
复制
发表时间：
2023-04-15
影响因子：
4.6
通讯作者：
Dalla Valle, Luciana
中科院分区：
文献类型：
--
作者：
Ansell, Lauren;Dalla Valle, Luciana
Covid-19大流行对人们的健康构成严重威胁，导致全球确诊病例超过2.5亿，死亡人数超过500万人。为了减轻国家医疗保健系统的负担并减轻疫情的影响，需要对短期和长期卫生需求进行准确的建模和预测方法，为政府旨在遏制疫情的干预措施提供信息。目前对 Covid-19 的研究通常基于单一信息源，特别是结构化的历史大流行数据。其他研究专门关注非结构化的在线检索见解，例如来自社交媒体的数据。然而，结构化和非结构化信息的组合使用仍然是未知的。本文旨在通过新颖的数据集成方法利用历史和社交媒体信息来填补这一空白。所提出的方法基于 vine copula，它允许我们利用不同信息源之间的依赖关系。我们应用该方法将从官方来源检索的结构化数据集和从社交媒体收集的大型非结构化信息数据集结合起来。结果表明，与单独使用官方数据相比，结合使用官方和在线生成的信息有助于对 Covid-19 大流行的演变进行更准确的评估。
The Covid-19 pandemic presents a serious threat to people’s health, resulting in over 250 million confirmed cases and over 5 million deaths globally. To reduce the burden on national health care systems and to mitigate the effects of the outbreak, accurate modelling and forecasting methods for short- and long-term health demand are needed to inform government interventions aiming at curbing the pandemic. Current research on Covid-19 is typically based on a single source of information, specifically on structured historical pandemic data. Other studies are exclusively focused on unstructured online retrieved insights, such as data available from social media. However, the combined use of structured and unstructured information is still uncharted. This paper aims at filling this gap, by leveraging historical and social media information with a novel data integration methodology. The proposed approach is based on vine copulas, which allow us to exploit the dependencies between different sources of information. We apply the methodology to combine structured datasets retrieved from official sources and a big unstructured dataset of information collected from social media. The results show that the combined use of official and online generated information contributes to yield a more accurate assessment of the evolution of the Covid-19 pandemic, compared to the sole use of official data.
登录
查看更多内容
DOI：
10.2196/31473
发表时间：
2022-01
期刊：
JMIR infodemiology
影响因子：
--
作者：
通讯作者：
--
DOI：
10.3390/ijerph17072365
发表时间：
2020-04-01
影响因子：
--
作者：
Qin, Lei;Sun, Qiang;Wu, Szu-Yuan
通讯作者：
Wu, Szu-Yuan
DOI：
10.1016/j.engappai.2017.08.006
发表时间：
2017-10-01
影响因子：
8
作者：
Hosseini, Akram Sadat
通讯作者：
Hosseini, Akram Sadat
影响因子：
1.8
作者：
Dissmann, J.;Brechmann, E. C.;Kurowicka, D.
通讯作者：
Kurowicka, D.
影响因子：
5.2
作者：
Maneejuk, Paravee;Thongkairat, Sukrit;Srichaikul, Wilawan
通讯作者：
Srichaikul, Wilawan