About: Using altmetrics for detecting impactful research in quasi-zero-day time-windows: the case of COVID-19

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type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Using altmetrics for detecting impactful research in quasi-zero-day time-windows: the case of COVID-19
Creator	Boetto, Erik Fantini, Maria Golinelli, Davide Greco, Manfredi Nuzzolese, Andrea Presutti, Valentina Rallo, Flavia Stlab, Aldo
source	ArXiv
abstract	On December 31st 2019, the World Health Organization (WHO) China Country Office was informed of cases of pneumonia of unknown etiology detected in Wuhan City. The cause of the syndrome was a new type of coronavirus isolated on January 7th 2020 and named Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2). SARS-CoV-2 is the cause of the coronavirus disease 2019 (COVID-19). Since January 2020 an ever increasing number of scientific works have appeared in literature. Identifying relevant research outcomes at very early stages is challenging.In this work we use COVID-19 as a use-case for investigating: (i) which toos and frameworkds are mostly used for early scholarly communication; (ii) to what extent altmetrics can be used to identify potential impactful research in tight (i.e. {/em quasi-zero-day}) time-windows. A literature review with rigorous eligibility criteria is performed for gathering a sample composed of scientific papers about SARS-CoV-2/COVID-19 appeared in literature in the tight time-window ranging from January 15th 2020 to February 24th 2020. This sample is used for building a knowledge graph that represents the knowledge about papers and indicators formally. This knowledge graph feeds a data analysis process which is applied for experimenting with altmetrics as impact indicators. We find moderate correlation among traditional citation count, citations on social media, and mentions on news and blogs. This suggests there is a common intended meaning of the citational acts associated with aforementioned indicators. Additionally, we define a method that harmonises different indicators for providing a multi-dimensional impact indicator.
has issue date	2020-04-13(xsd:dateTime)
has license	arxiv
sha1sum (hex)	2c837018d332088b3b45f0b6189d1df3cb53064d
resource representing a document's title	Using altmetrics for detecting impactful research in quasi-zero-day time-windows: the case of COVID-19
resource representing a document's body	covid:2c837018d332088b3b45f0b6189d1df3cb53064d#body_text
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