ABSTRACT
Urgent global research demands real-time dissemination of precise data. Wikidata, a collaborative and openly licensed knowledge graph available in RDF format, provides an ideal forum for exchanging structured data that can be verified and consolidated using validation schemas and bot edits. In this research article, we catalog an automatable task set necessary to assess and validate the portion of Wikidata relating to the COVID-19 epidemiology. These tasks assess statistical data and are implemented in SPARQL, a query language for semantic databases. We demonstrate the efficiency of our methods for evaluating structured non-relational information on COVID-19 in Wikidata, and its applicability in collaborative ontologies and knowledge graphs more broadly. We show the advantages and limitations of our proposed approach by comparing it to the features of other methods for the validation of linked web data as revealed by previous research.
ABSTRACT
In this research letter, we build upon recent studies about the sleeping beauties awakened by the COVID-19 pandemic. We prove that a peak of citations for sleeping beauties is associated with a sharp increase in the number of citations received by their references. This demonstrates the existence of a cascading activation of citation-based sleeping beauties.
ABSTRACT
During the last years, several infectious diseases have caused widespread nationwide epidemics that affected information seeking behaviours, people mobility, economics and research trends. Examples of these epidemics are 2003 severe acute respiratory syndrome (SARS) epidemic in mainland China and Hong Kong, 2014–2016 Ebola epidemic in Guinea and Sierra Leone, 2015–2016 Zika epidemic in Brazil, Colombia and Puerto Rico and the recent COVID-19 epidemic in China and other countries. In this research article, we investigate the effect of large-scale outbreaks of infectious diseases on the research productivity and landscape of nations through the analysis of the research outputs of main countries affected by SARS, Zika and Ebola epidemics as returned by Web of Science Core Collection. Despite the mobility restrictions and the limitations of work conditions due to the epidemics, we surprisingly found that the research characteristics and productivity of the countries that have excellent or moderate research traditions and communities are not affected by infectious epidemics due to their robust long-term research structures and policy. Similarly, large-scale infectious outbreaks can even boost the research productivity of countries with limited research traditions thanks to international capacity building collaborations provided by organisations and associations from leading research countries. [ABSTRACT FROM AUTHOR] Copyright of Journal of Information Science is the property of Sage Publications, Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
ABSTRACT
Social data has shown important role in tracking, monitoring and risk management of disasters. Indeed, several works focused on the benefits of social data analysis for the healthcare practices and curing domain. Similarly, these data are exploited now for tracking the COVID-19 pandemic but the majority of works exploited Twitter as source. In this paper, we choose to exploit Facebook, rarely used, for tracking the evolution of COVID-19 related trends. In fact, a multilingual dataset covering 7 languages (English (EN), Arabic (AR), Spanish (ES), Italian (IT), German (DE), French (FR) and Japanese (JP)) is extracted from Facebook public posts. The proposal is an analytics process including a data gathering step, pre-processing, LDA-based topic modeling and presentation module using graph structure. Data analysing covers the duration spanned from January 1st, 2020 to May 15, 2020 divided on three periods in cumulative way: first period January-February, second period March-April and the last one to 15 May. The results showed that the extracted topics correspond to the chronological development of what has been circulated around the pandemic and the measures that have been taken according to the various languages under discussion representing several countries.