COVID-19 and Official Statistics: A Wakeup Call?

Biancotti, Claudia; Rosolia, Alfonso; Veronese, Giovanni; Kirchner, Robert; Mouriaux, Francois

doi:10.2139/ssrn.3828122

Cited by 12 publications

(10 citation statements)

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“…Such cross-region heterogeneities are further documented in our computation of the buyers' negotiation margin. This paper contributes to the literature, first by providing new evidence for the ongoing move to higher frequency statistics relying to some extent on alternative data (see Veronese et al, 2020). Rather than competing with official statistics, our web-scraped data however complement the latter by detecting trends with enhanced timeliness (daily indices in real-time vs. monthly / quarterly with lag) and high granularity (ZIP code level vs. at best region-level).…”

Section: Introductionmentioning

confidence: 82%

Web Scraping Housing Prices in Real-time: the Covid-19 Crisis in the UK

Bricongne

Meunier

2021

SSRN Journal

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Section: Introductionmentioning

confidence: 82%

Web Scraping Housing Prices in Real-time: the Covid-19 Crisis in the UK

Bricongne

Meunier

2021

SSRN Journal

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“…While official data provide a solid and structured database, real-time social network information reflects the opinions and emotions of society more immediately and dynamically [41]. This combination allows the capture of events and tendencies in real-time, complementing official information that may suffer from a time lag [42].…”

Section: Discussionmentioning

confidence: 99%

Profiling Social Sentiment in Times of Health Emergencies with Information from Social Networks and Official Statistics

Velasco-López,

Carrasco,

Serrano-Guerrero

et al. 2024

Mathematics

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Social networks and official statistics have become vital sources of information in times of health emergencies. The ability to monitor and profile social sentiment is essential for understanding public perception and response in the context of public health crises, such as the one resulting from the COVID-19 pandemic. This study will explore how social sentiment monitoring and profiling can be conducted using information from social networks and official statistics, and how this combination of data can offer a more complete picture of social dynamics in times of emergency, providing a valuable tool for understanding public perception and guiding a public health response. To this end, a three-layer architecture based on Big Data and Artificial Intelligence is presented: the first layer focuses mainly on collecting, storing, and governing the necessary data such as social media and official statistics; in the second layer, the representation models and machine learning necessary for knowledge generation are built, and in the third layer the previously generated knowledge is adapted for better understanding by crisis managers through visualization techniques among others. Based on this architecture, a KDD (Knowledge Discovery in Databases) framework is implemented using methodological tools such as sentiment analysis, fuzzy 2-tuple linguistic models and time series prediction with the Prophet model. As a practical demonstration of the proposed model, we use tweets as data source (from the social network X, formerly known as Twitter) generated during the COVID-19 pandemic lockdown period in Spain, which are processed to identify the overall sentiment using sentiment analysis techniques and fuzzy linguistic variables, and combined with official statistical indicators for prediction, visualizing the results through dashboards.

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“…It also underlined the importance of high-frequency, well documented and timely indicators to support evidence-based policy, calling for statistical frameworks to become more flexible and granular with the aim of addressing the evolving needs of users and help them monitor fragilities [9]. Another lesson is that the (unexpected) nature of the shock has clearly expanded the range of statistics that public authorities must look at, and highlighted the need to look at less conventional and still untapped sources of alternative information [10].…”

Section: Holistic Approach To Data Governancementioning

confidence: 99%

Data governance frameworks for official statistics and the integration of alternative sources

Tissot¹,

Križman

2022

SJI

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Well-defined data governance frameworks have proved instrumental in supporting official statisticians’ task to collect and analyse data of the highest quality possible. However, an institution-level approach to data governance should be complemented by a broader focus covering the entire production and use of national statistics, including alternative sources. For instance, ensuring the following of adequate Codes of Principles by private data providers, clarifying the responsibilities in the national governance landscape, and establishing proper international guidelines and cooperation mechanisms. Moreover, while national statistical systems are facing a decline in their traditional function of “data collectors”, they have a key role to play as reference custodians of the quality of the data used by society. Establishing sound data governance frameworks based on widely accepted standards can be a central element in supporting this “data curator approach”.

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COVID-19 and Official Statistics: A Wakeup Call?

Cited by 12 publications

References 0 publications

Web Scraping Housing Prices in Real-time: the Covid-19 Crisis in the UK

Web Scraping Housing Prices in Real-time: the Covid-19 Crisis in the UK

Profiling Social Sentiment in Times of Health Emergencies with Information from Social Networks and Official Statistics

Data governance frameworks for official statistics and the integration of alternative sources

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