Abstract
On December 31st 2019, the World Health Organization 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 related to the new pathogen 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: (1) which tools and frameworks are mostly used for early scholarly communication; (2) to what extent altmetrics can be used to identify potential impactful research in tight (i.e. 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. Additionally, correlation coefficients are not inflated by indicators associated with zero values, which are quite common at very early stages after an article has been published. This suggests there is a common intended meaning of the citational acts associated with aforementioned indicators. Then, we define a method, i.e. the Comprehensive Impact Score (CIS), that harmonises different indicators for providing a multi-dimensional impact indicator. CIS shows promising results as a tool for selecting relevant papers even in a tight time-window. Our results foster the development of automated frameworks aimed at helping the scientific community in identifying relevant work even in case of limited literature and observation time.
Highlights
A zero-day attack is a cyber attack exploiting a vulnerability of a computer-software that is either unknown or it has not been disclosed publicly (Bilge and Dumitras 2012)
In this work we investigate altmetrics and citation count as tools for detecting impactful research works in quasi-zero-day time-windows, as it is for the case of COVID-19
Our case-study relies on a sample of 212 scientific papers on COVID-19 collected by means of a literature review
Summary
A zero-day attack is a cyber attack exploiting a vulnerability (i.e. zero-day vulnerability) of a computer-software that is either unknown or it has not been disclosed publicly (Bilge and Dumitras 2012). COVID-19 is a zero-day attack where the target system is the human immune system and the attacker is SARS-CoV-2. Altmetrics might be valid tools for measuring the impact in quasi-zero-day time-window. For the first time in human history, we are facing a pandemic, which is described, debated, and investigated in real time by the scientific community via conventional research venues (i.e. journal papers), jointly with social and on-line media. RQ2: How is it possible to use altmetrics for automatically identifying candidate impactful research works in quasi-zero-day time-windows?. For answering aforementioned research questions we carry out an experiment by using a sample of 212 papers on COVID-19. This sample has been collected by means of a rigorous literature review. The rest of the paper is organised in the following way: “Related work” section presents related work; “Material and method” section describes the material and method used for the experiments; “Data analysis” section presents the data analysis we perform and the results we record; “Discussion” section discusses the results; “Conclusions and future work” section presents our conclusions and future work
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