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
Information related to the COVID-19 pandemic ranges from biological to bibliographic, from geographical to genetic and beyond. The structure of the raw data is highly complex, so converting it to meaningful insight requires data curation, integration, extraction and visualization, the global crowdsourcing of which provides both additional challenges and opportunities. Wikidata is an interdisciplinary, multilingual, open collaborative knowledge base of more than 90 million entities connected by well over a billion relationships. It acts as a web-scale platform for broader computer-supported cooperative work and linked open data, since it can be written to and queried in multiple ways in near real time by specialists, automated tools and the public. The main query language, SPARQL, is a semantic language used to retrieve and process information from databases saved in Resource Description Framework (RDF) format. Here, we introduce four aspects of Wikidata that enable it to serve as a knowledge base for general information on the COVID-19 pandemic: its flexible data model, its multilingual features, its alignment to multiple external databases, and its multidisciplinary organization. The rich knowledge graph created for COVID-19 in Wikidata can be visualized, explored, and analyzed for purposes like decision support as well as educational and scholarly research.
ABSTRACT
Social data has shown important role in tracking, monitoring and risk management of disasters. Indeed, several works focused on the benets of social data analysis to the healthcare practices and curing. 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 modelling 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: rst 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 in the various languages under discussion.