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.

中文翻译：

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使用 altmetrics 在准零日时间窗中检测有影响力的研究：以 COVID-19 为例

2019年12月31日，世界卫生组织驻华代表处获悉武汉市发现不明原因肺炎病例。该综合征的病因是 2020 年 1 月 7 日分离出的一种新型冠状病毒，被命名为严重急性呼吸系统综合症冠状病毒 2（SARS-CoV-2）。SARS-CoV-2 是 2019 年冠状病毒病 (COVID-19) 的病因。自 2020 年 1 月以来，文献中出现了越来越多与新病原体相关的科学著作。在非常早的阶段确定相关的研究成果是具有挑战性的。在这项工作中，我们使用 COVID-19 作为用例来调查：（1）哪些工具和框架主要用于早期学术交流；(2) altmetrics 在何种程度上可用于识别紧缩（即准零日）时间窗口中潜在的有影响力的研究。对 2020 年 1 月 15 日至 2020 年 2 月 24 日这段时间紧迫的文献中出现的有关 SARS-CoV-2/COVID-19 的科学论文组成的样本进行了具有严格资格标准的文献审查。该样本用于用于构建一个知识图谱，正式表示有关论文和指标的知识。该知识图提供了一个数据分析过程，该过程用于试验 altmetrics 作为影响指标。我们发现传统引文计数、社交媒体上的引文以及新闻和博客上的提及之间存在适度的相关性。此外，相关系数不会被与零值相关的指标夸大，这在文章发表后的早期阶段很常见。这表明与上述指标相关的引证行为具有共同的预期含义。然后，我们定义了一种方法，即综合影响评分（CIS），它可以协调不同的指标以提供多维影响指标。即使在紧迫的时间窗口中，CIS 作为选择相关论文的工具也显示出有希望的结果。我们的结果促进了自动化框架的开发，旨在帮助科学界即使在文献和观察时间有限的情况下也能识别相关工作。即使在紧迫的时间窗口中，CIS 作为选择相关论文的工具也显示出有希望的结果。我们的结果促进了自动化框架的开发，旨在帮助科学界即使在文献和观察时间有限的情况下也能识别相关工作。即使在紧迫的时间窗口中，CIS 作为选择相关论文的工具也显示出有希望的结果。我们的结果促进了自动化框架的开发，旨在帮助科学界即使在文献和观察时间有限的情况下也能识别相关工作。