Augmenting business statistics information by combining traditional data with textual data: a composite indicator approach
被引:0
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作者:
Camilla Salvatore
论文数: 0引用数: 0
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机构:Utrecht University,Department of Methodology and Statistics
Camilla Salvatore
Silvia Biffignandi
论文数: 0引用数: 0
h-index: 0
机构:Utrecht University,Department of Methodology and Statistics
Silvia Biffignandi
Annamaria Bianchi
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h-index: 0
机构:Utrecht University,Department of Methodology and Statistics
Annamaria Bianchi
机构:
[1] Utrecht University,Department of Methodology and Statistics
[2] Consultant in Economic Statistics Studies,Department of Economics
[3] University of Bergamo,undefined
来源:
METRON
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2024年
/
82卷
关键词:
Socio-economic indicators;
Mazziotta–Pareto index;
Sustainable development;
Social media;
Twitter;
D O I:
暂无
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学科分类号:
摘要:
Combining traditional and digital trace data is an emerging trend in statistics. In this respect, new data sources represent the basis for multi-purpose extraction of different statistical indicators, which contribute to augmenting the statistical information, for feeding smart statistics. The production of business statistics can benefit from the use of unstructured data, especially to study novel aspects which are not covered by traditional data sources. This paper proposes a methodological general framework for augmenting information by combining data, both structured and non structured. The statistical challenges of using unstructured data and their integration with traditional data are discussed. The methodological general framework is applied to the construction of smart composite indicators using social media data and their metadata. An empirical exercise illustrates how to apply the methodology in practice.
机构:
Univ Washington, Dept Commun, Seattle, WA USA
Univ Washington, Dept Commun, 102 Commun,Box 353740, Seattle, WA 98195 USAUniv Washington, Dept Commun, Seattle, WA USA
Hsiao, Yuan
Fiorio, Lee
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机构:
Univ Washington, Dept Geog, Seattle, WA USAUniv Washington, Dept Commun, Seattle, WA USA
Fiorio, Lee
Wakefield, Jonathan
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h-index: 0
机构:
Univ Washington, Dept Stat, Seattle, WA USA
Univ Washington, Dept Biostat, Seattle, WA USAUniv Washington, Dept Commun, Seattle, WA USA
Wakefield, Jonathan
Zagheni, Emilio
论文数: 0引用数: 0
h-index: 0
机构:
Max Planck Inst Demog Res, Rostock, GermanyUniv Washington, Dept Commun, Seattle, WA USA
机构:
Tsinghua Univ, Beijing 100084, Peoples R China
Beijing Key Lab City Integrated Emergency Respons, Beijing 100084, Peoples R ChinaTsinghua Univ, Beijing 100084, Peoples R China
Deng, Qing
Gao, Yang
论文数: 0引用数: 0
h-index: 0
机构:
Tsinghua Univ, Beijing 100084, Peoples R China
Beijing Key Lab City Integrated Emergency Respons, Beijing 100084, Peoples R ChinaTsinghua Univ, Beijing 100084, Peoples R China
Gao, Yang
Wang, Chenyang
论文数: 0引用数: 0
h-index: 0
机构:
Tsinghua Univ, Beijing 100084, Peoples R China
Beijing Key Lab City Integrated Emergency Respons, Beijing 100084, Peoples R ChinaTsinghua Univ, Beijing 100084, Peoples R China
Wang, Chenyang
Zhang, Hui
论文数: 0引用数: 0
h-index: 0
机构:
Tsinghua Univ, Beijing 100084, Peoples R China
Beijing Key Lab City Integrated Emergency Respons, Beijing 100084, Peoples R ChinaTsinghua Univ, Beijing 100084, Peoples R China