RESEARCH ARTICLE

COVID-19 publications: Database coverage,
citations, 读者, tweets, 消息,
Facebook walls, Reddit posts

开放访问

杂志

Statistical Cybermetrics Research Group, University of Wolverhampton, Wulfruna Street, Wolverhampton WV1 1LY, 英国

Kayvan Kousha

and Mike Thelwall

关键词: altmetrics, COVID-19, Dimensions, Mendeley, Scopus, Web of Science

抽象的

The COVID-19 pandemic requires a fast response from researchers to help address biological,
medical, and public health issues to minimize its impact. In this rapidly evolving context,
学者, professionals, and the public may need to identify important new studies quickly. 在
response, this paper assesses the coverage of scholarly databases and impact indicators during
行进 21, 2020 to April 18, 2020. The rapidly increasing volume of research is particularly
accessible through Dimensions, and less through Scopus, the Web of Science, and PubMed.
Google Scholar’s results included many false matches. A few COVID-19 papers from the
21,395 in Dimensions were already highly cited, with substantial news and social media
注意力. For this topic, in contrast to previous studies, there seems to be a high degree of
convergence between articles shared in the social web and citation counts, at least in the short
学期. 尤其, articles that are extensively tweeted on the day first indexed are likely to
be highly read and relatively highly cited 3 weeks later. Researchers needing wide scope
literature searches (rather than health-focused PubMed or medRxiv searches) should start with
Dimensions (or Google Scholar) and can use tweet and Mendeley reader counts as indicators
of likely importance.

介绍

The international scientific effort to mitigate COVID-19 is unprecedented in scale and rapidity.
例如, PubMed added related publications daily between January 17 and April 18, 20201
(数字 1), reaching over 300 in a single day. This effort is in response to the lethality and rapid
spread of the disease, as well as the major economic and social consequences of COVID-19
lockdowns. As part of the response, 研究人员, professionals, and the public may need to
consult the scientific literature for the latest findings. Although this is normal for science,
standard literature search methods may be ineffective in a rapid publishing environment.
Traditional citation indexes may not be fast enough, especially given that they do not index most
preprints, and citation counts may not help point to important studies. The more inclusive online
citation indexes of sites such as Google Scholar and Dimensions.ai seem like suitable alterna-
tives because they index both the traditional scholarly literature and documents not published in
journals, including preprints (Herzog, Hook, & Konkiel, 2020; Kousha & Thelwall, 2019A).
There are initiatives to help various communities with curated collections of COVID-19

1 https://www.nlm.nih.gov/pubs/techbull/nd08/nd08_pm_new_date_field.html

引文: Kousha, K., & Thelwall, 中号.
(2020). COVID-19 publications:
Database coverage, citations, 读者,
tweets, 消息, Facebook walls, 红迪网
posts. Quantitative Science Studies,
1(3), 1068–1091. https://doi.org/10.1162/
qss_a_00066

DOI:
https://doi.org/10.1162/qss_a_00066

支持信息:
https://doi.org/10.1162/qss_a_00066

已收到: 21 四月 2020
公认: 14 可能 2020

通讯作者:
Mike Thelwall
m.thelwall@wlv.ac.uk

处理编辑器:
Ludo Waltman

版权: © 2020 Kayvan Kousha and
Mike Thelwall. Published under a
Creative Commons Attribution 4.0
国际的 (抄送 4.0) 执照.

麻省理工学院出版社

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COVID-19 publications

文件, such as published biomedical documents from PubMed Central (PMC, 2020),
preprints from medRxiv and bioRxiv (medRxiv, 2020), and a data mining collection (艾伦
研究所, 2020; Colavizza, Costas, 等人。, 2020), but none are complete. It is therefore important
to assess the COVID-19 coverage and growth of scholarly publication indexes, as well as the
value of citation counts for new COVID-19 research.

In parallel with scholarly needs for literature, the public, professionals, and policy-makers also
need to access current COVID-19 research to inform their decision-making, such as whether to
recommend wearing protective masks. This may be in addition to, or to clarify, World Health
Organization guidelines (WHO, 2020). They may therefore share relevant academic research
in the social web (例如, Merchant & Lurie, 2020), generating interest that may picked up by alter-
native indicators (altmetrics). 因此, altmetrics may be useful in helping the public to identify the
most relevant research or may help point researchers to topics considered important by the public.
It would therefore be helpful to assess whether altmetrics can perform this role. 尤其, 是-
cause altmetrics can reflect both academic and nonacademic interests (Mohammadi, Barahmand,
& Thelwall, 2019; Mohammadi, Thelwall, 等人。, 2018), it is not clear whether they will essentially
be early indicators of citation impact or whether they reflect societal or other impacts for COVID-
19. Altmetrics have already been shown useful to identify the spread of a misleading COVID-19
paper that was subsequently withdrawn (Ioannidis, 2020).

This paper addresses the above issues through a primarily descriptive analysis of the evolution
of four online scholarly databases, and associated altmetrics, 超过 4 weeks in March–April
2020, when many countries were experiencing a lockdown. A previous study of January 20 到
四月 12, 2020 has shown continually increasing growth in the COVID-19 coverage of scholarly
databases, with substantial variations between fields (Torres-Salinas, 2020). Individual highly cited

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数字 1. Daily additions of COVID-19 publications to PubMed ( 一月 17 to April 18). Query
用过的 (((((((“COVID-19”) 或者 “Novel coronavirus”) 或者 “2019-nCoV”) 或者 “SARS-CoV-2”) 或者
“coronavirus 2”) 或者 “Coronavirus disease 2019”) 或者 “Corona virus disease 2019”) AND (“2019/12/
01″[日期 – Publication] : “3000”[日期 – Publication]).

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COVID-19 publications

or shared papers are also important to examine for qualitative insights into the types of research
that are attracting substantial attention. The following research questions drive this paper:

(西德:129) Which scholarly databases index the most COVID-19 publications (extending: Torres-

Salinas, 2020)?

(西德:129) Which COVID-19 documents have become highly cited or highly discussed?
(西德:129) Do altmetrics and early citation counts reflect similar types of COVID-19 impact?
(西德:129) Can any altmetrics serve as early indicators of future citation impact for COVID-19

文件?

2. 背景

The novel coronavirus SARS-CoV-2, which causes COVID-19, was first recorded in Wuhan
城市, China in December 2019. Quickly disseminating scientific results about COVID-19 is
vital to allow the rapid exploitation of successful clinical results (歌曲 & Karako, 2020). 这
importance of scientific publishing to respond to infectious disease outbreaks has been em-
phasized by many bibliometric studies of previous cases (Rethlefsen & Livinski, 2013), 这样的
as SARS (Kostoff & Morse, 2011; Tian & 郑, 2015), H7N9 influenza (Tian & 郑, 2015),
HIV/AIDS (Pouris & Pouris, 2011), Ebola (Pouris & Ho, 2016), and Zika (Delwiche, 2018).

One recent study using Dimensions, Scopus, Web of Science ( WoS), and the LitCovid (陈,
Allot, & 鲁, 2020) curated list has investigated the daily growth of COVID-19 related publications
in citation databases and digital libraries from January 1 to April 7, finding that Dimensions had
the best coverage (9,435 出版物) compared to WoS (718) and Scopus (1,568). The weekly
growth of PubMed was about 1,000 publications and the PubMed Central (1,398), medRxiv (989),
and SSRN (608) repositories had the best coverage of open access COVID-19 publications (Torres-
Salinas, 2020). Google Scholar was not assessed, and all evidence was extracted from
Dimensions, so the counts for other repositories may not be complete.

2.1. Dimensions Citations

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Dimensions.ai (Herzog et al., 2020) is an online scholarly database that operates similarly to
Google Scholar, in the sense of indexing documents using public information from the Web,
but has an Applications Programming Interface (应用程序编程接口) that supports automatic downloading for
all query matches. It indexes most documents in Scopus (Thelwall, 2018乙), although not for all
fields (Orduña-Malea & Delgado-López-Cózar, 2018). It seems to have substantial coverage of
preprint servers, such as arXiv, and so probably has much larger coverage overall, 尤其
for recently published papers. Its coverage seems to be higher than Scopus and WoS, compa-
rable to CrossRef but lower than Google Scholar and Microsoft Academic (Harzing, 2019). 在
line with this, citation counts for papers in Dimensions can be expected to be slightly higher
than for Scopus and WoS but substantially higher for newer documents.

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2.2. Altmetrics: Mendeley Readers

Counts of readers from the social reference sharing site Mendeley form the most extensively
researched and understood altmetric. A nontrivial minority of researchers (关于 5%) 用过的
Mendeley by 2014 according to one survey, with disciplinary differences (Van Noorden, 2014).
People typically register documents in Mendeley when they have read them or intend to read them
(Mohammadi, Thelwall, & Kousha, 2016), so it is reasonable to regard Mendeley counts as an
indicator of readership. According to self-reports in the site, users are predominantly academics
and postgraduate students, with a few undergraduates, librarians, and people in nonacademic

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occupations (Mohammadi, Thelwall, 等人。, 2015). 因此, Mendeley is an indicator of predominantly
academic readership, with an element of student readership. One difference is that nonarticle pub-
lications in journals, such as editorials and news items, are relatively more likely to be registered in
Mendeley than to be cited (Zahedi & Haustein, 2018). Mendeley reader counts can help with the
early identification of highly cited documents (Zahedi, Costas, & Wouters, 2017).

A range of studies have investigated the relationship between Mendeley reader counts and
citation counts, finding moderate or strong positive correlations (Costas, Zahedi, & Wouters,
2015). Correlations between mature citation counts and Mendeley reader counts are strong
and positive in almost all narrow fields in Scopus (Thelwall, 2017A), supporting their use as a
citation impact type of indicator. Although the two types of data seem to be close to interchange-
able for sets of mature articles (although they can differ sharply for individual education-oriented
文件: Thelwall, 2017C), the advantage of Mendeley reader counts is that they appear and are
useful a year before citation counts (Thelwall, 2017乙). They may even be common enough to be
used for scientometric purposes by the publication month of the publishing journal. 而且,
as early Mendeley reader counts correlate positively with later citation counts (Thelwall, 2018A),
Mendeley reader counts are early academic impact indicators. They should therefore be a better
academic impact indicator than citation counts for fast-moving issues, such as COVID-19.

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2.3. Altmetrics: Tweeters, Facebook Walls

Twitter is potentially a source of societal attention evidence (Holmberg & Vainio, 2018; Priem,
Taraborelli, 等人。, 2010). More articles have nonzero tweet counts than nonzero scores on any other
altmetric, other than Mendeley (Costas et al., 2015; Thelwall, Haustein, 等人。, 2013). As Twitter is a
news-oriented social media platform, articles can expect to get a substantial proportion of their
tweets in the week of publication, so tweets are visible long before citations (Ortega, 2018A,乙).

Tweeter counts (counting the number of tweeters rather than the number of tweets) are prob-
lematic to interpret. About half of people that tweet academic research are not academics
(Mohammadi et al., 2018), and tweets typically contain just article titles or brief summaries
(Thelwall, Tsou, 等人。, 2013; Robinson-García, Costas, 等人。, 2017), serving as publicity rather
than evidence of impact. Many academic tweets are also created by bots (Haustein, Bowman,
等人。, 2016; Robinson-García, Costas, 等人。, 2017). Together with often close to zero correla-
tions with citation counts (Costas et al., 2015; Haustein, Larivière, 等人。, 2014; Thelwall,
Haustein, 等人。, 2013), there is insufficient evidence to claim that tweeter counts are indicators
of either academic or societal impact. 尽管如此, they may have some value for health-
related research, where there is more public interest in academic research (Haustein, Larivière,
等人。, 2014; Mohammadi, Gregory, 等人。, 2020). Editorials and news articles are relatively more
likely to be tweeted than cited (Haustein, Costas, & Larivière, 2015), reflecting the news orien-
tation of Twitter.

Facebook wall posts function like tweeter counts except that they are rarer (Costas et al., 2015;
Thelwall, Haustein, 等人。, 2013). As most of Facebook is private and Altmetric.com obtains its
Facebook wall counts only from public pages, this altmetric probably reflects a tiny fraction of
all Facebook posts and may be oriented to organizational uses of Facebook (including journals)
rather than typical users; few posts are directly from academics (Mohammadi et al., 2019).

2.4. Altmetrics: News and Reddit

Altmetric.com harvests citations from online free news websites and the news-oriented site
红迪网. Altmetrics from both are relatively rare and have very low correlations with citation

Quantitative Science Studies

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COVID-19 publications

计数 (Costas et al., 2015; Thelwall, Haustein, 等人。, 2013). 尽管如此, health-related
topics are newsworthy (克拉克 & Illman, 2006; Kousha & Thelwall, 2019乙), including for infec-
tious diseases (例如, SARS: Lewison, 2008), so they may be useful for COVID-19.

3. 方法

The research design is in three parts. 第一的, to assess the relative coverage of scholarly data-
bases, the main candidates were queried daily from March 21, 2020 to record the number
of COVID-19 documents indexed. 第二, lists of documents matching a set of COVID-19
queries were downloaded from Dimensions.ai and altmetrics for these were gathered from
Mendeley (Gunn, 2014) and Altmetric.com (Adie & Roe, 2013; Robinson-García, Torres-
Salinas, 等人。, 2014) daily and the individual scores and documents compared. 第三, A
行进 24 data set was created to track a set of documents indexed on the same day.

3.1. Scholarly Database Indexing of COVID-19 Publications

To assess the indexing of COVID-19-related publications, the two mainstream scholarly data-
bases, Scopus and WoS, were queried as well as other major academic sources that may index
relevant documents. After testing with the original and current names of the virus and disease
and “Corona virus disease 2019” and “Coronavirus disease 2019”, the core queries used to
identify relevant documents were as shown in Table 1. The queries are designed to be as in-
clusive as possible for the database in terms of document type and part of the document
searched: full text, if available in the database, otherwise all metadata fields (例如, title, ab-
stract, keywords). The queries are not comprehensive but are high precision, unless stated,
and should include the most recent research focusing on the issue, assuming that it includes
the current official disease description.

The combined queries did not work in Google Scholar, giving false matches. The results for
Google Scholar seemed to be substantially inflated by its web search component indexing
advertisements or warnings in webpages alongside articles irrelevant to the disease, so its re-
sults are not reported. To illustrate the existence of these false matches, a search for “COVID-
19” in Google Scholar with a date range specified as 1990–2000 (IE。, 20 years before the
name was coined) on April 21, 2020 returned an estimated 5,010 matches2. Each incorrect
Google Scholar match reported snippets not from the paper, 例如, “PEDIATRICS COVID-19
COLLECTION We are fast-tracking and publishing the latest research and articles related to
COVID-19 for free.” The exact COVID-19 coverage of Google Scholar is difficult to assess
because it is not possible to download and check all matches in the absence of a Google
Scholar API to download large sets of publication records. Because of these issues, no results
are reported for Google Scholar.

3.2. Document and Altmetric Comparison Data Sets

Initial testing suggested that Dimensions and Google Scholar had the largest coverage of
COVID-19 documents. As Google Scholar does not have an API and the number of matches
exceeds its 1,000 limit per query, it was not possible to extract Google Scholar’s set of matching
文件. 相比之下, Dimensions.ai has an API allowing complete sets of matching document
records to be downloaded and does not seem to include false matches, so it was chosen as the
base source of COVID-19 documents. It was checked daily with the following set of queries in
the Dimensions API, designed to match publications about COVID-19 using various related

2 https://scholar.google.co.uk/scholar?q=covid-19&hl=en&as_sdt=0%2C5&as_ylo=1990&as_yhi=2000

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COVID-19 publications

桌子 1. COVID-19 queries for a range of scholarly sources

来源
Google Scholar

“COVID-19”

Query

Dimensions

“COVID-19” 或者 “Novel coronavirus” 或者 “2019-nCoV”

或者 “SARS-CoV-2” 或者 “coronavirus 2” 或者
“Coronavirus disease 2019” 或者 “Corona virus
disease 2019”

考研

(((((((“COVID-19”) 或者 “Novel coronavirus”) 或者

“2019-nCoV”) 或者 “SARS-CoV-2”) 或者 “coronavirus 2”)
或者 “Coronavirus disease 2019”) OR “Corona virus
disease 2019”) AND (“2019/12/01″[日期 – Publication] :
“3000”[日期 – Publication])

评论
OR does not work
False matches.

Scope/ Year

Full text

2019–2020

Full text

2019–2020

All metadata from

Dec 2019

Mendeley

“COVID-19”

Probably metadata3 OR does not work.

medRxiv and
bioRxiv

Scopus

WoS Core

PMC

Self-reported repository statistics for self-curated collection.

Full text

Repository statistics for

COVID-19 SARS-CoV-2
preprints from medRxiv
and bioRxiv4.

(ALL (“COVID-19”) OR ALL (“Novel coronavirus”) 或者
ALL (“2019-nCoV”) OR ALL (“SARS-CoV-2”) OR ALL
(“coronavirus 2”) OR ALL (“Coronavirus disease 2019”)
OR ALL (“Corona virus disease 2019”)) AND
PUBYEAR = 2020 OR PUBDATETXT (december 2019)

All metadata
2019–2020

TOPIC=(“COVID-19” 或者 “Novel coronavirus” 或者

“2019-nCoV” 或者 “SARS-CoV-2” 或者 “coronavirus 2”
或者 “Coronavirus disease 2019” 或者 “Corona virus
disease 2019”)

((((((((“COVID-19”) 或者 “Novel coronavirus”) 或者 “Novel
coronavirus”) 或者 “2019-nCoV”) 或者 “SARS-CoV-2”)
或者 “coronavirus 2”) 或者 “Coronavirus disease 2019”)
或者 “Corona virus disease 2019”) AND
(“2019/12/01″[Publication Date] :
“3000”[Publication Date])

All metadata
2019–2020

Including Conference

Proceedings Citation
Index.

Full text from
Dec 2019

ClinicalTrials.gov COVID OR “SARS-CoV-2” 或者 “2019-nCoV”

Query predefined statistics5.

名字. These queries are all designed to be precise, but there were still a few false matches. 全部
queries ended in “return publications [basics + extras]”.

(西德:129) search publications for “COVID-19” where year >= 2019
(西德:129) search publications for “Novel coronavirus” where year >= 2019
(西德:129) search publications for “2019-nCoV” where year >= 2019
(西德:129) search publications for “SARS-CoV-2” where year >= 2019
(西德:129) search publications for “coronavirus 2” where year >= 2019

3 Mendeley does not report the scope of its search feature https://www.mendeley.com/guides/web/02-paper-

搜索

4 https://connect.biorxiv.org/relate/content/181
5 https://clinicaltrials.gov/ct2/results?cond=COVID-19

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COVID-19 publications

桌子 2. The number of articles for the top 10 Dimensions subject codes for the complete and March 24 data sets

FOR code
1117 Public Health and Health Services

1108 Medical Microbiology

1103 Clinical Sciences

0601 Biochemistry and Cell Biology

1107 Immunology

0604 遗传学

1102 Cardiorespiratory Medicine & Haematology

0801 Artificial Intelligence and Image Processing

1109 神经科学

0605 Microbiology

* Counting 1/n for a paper with n subject codes.

全部
3,072

2,773

2,159

1,192

1,096

803

459

383

316

364

全部 (frac)*
2,762.9

2,240.8

1,860.9

946.3

873.4

642.7

384.4

336.0

257.9

224.3

%
13%

10%

Mar-24
78

Mar-24 (frac)*
73.3

%
21%

27.2

28.8

11.3

2.8

6.5

6.0

1.3

0.0

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(西德:129) search publications for “Coronavirus disease 2019” where year >= 2019
(西德:129) search publications for “Corona virus disease 2019” where year >= 2019

The resulting 21,395 publications were mainly open access (53%; 75% for the March 24

set—see later) and predominantly from health-related specialties (桌子 2).

The data sets analyzed include substantial numbers of papers from preprint planforms, 在-
cluding medRxiv, SSRN, arXiv, bioRxiv, ChemRxiv, and Research Square (桌子 3, as in Torres-
Salinas, 2020), as well as books and more traditional journals (桌子 3).

Although most documents were classified as Articles by Dimensions, this type includes
medRxiv preprints and diverse types of document published in journals, such as notes, short

桌子 3. The top 10 journals, as recorded in Dimensions, for the complete and March 24 data sets

杂志
[没有任何]

medRxiv

SSRN Electronic Journal

arXiv

bioRxiv

Research Square

BMJ

ChemRxiv

Viruses

Journal of Medical Virology

全部
2,932

1,234

855

389

358

341

262

210

196

176

%
14%

Mar-24
13

%
4%

Comment
图书, book chapters, 论文

Health sciences preprints

Social science preprints

Physics/computing preprints

Biological sciences preprints

Preprint platform

Core medical journal

Chemistry preprints

MDPI open access journal

Wiley journal

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COVID-19 publications

桌子 4. The top 10 document types, as recorded in Dimensions, for the complete and March 24 data sets

Type
文章

书

Chapter

Preprint

Monograph

Proceeding

全部
16,330

832

1,645

2,236

166

186

%
76%

10%

Mar-24
295

%
85%

Includes preprints from medRxiv, editorials, commentaries

Matches more general “Coronavirus 2” research

12%

Includes arRxiv, Research Square, chemRxiv, JMIR Preprints, SSRN

Matches more general “Coronavirus 2” research

Conference proceedings

全部的

21,392

100%

349

100%

通讯, editorials, and commentaries (桌子 4). As many editorials seemed to discuss
the impact of COVID-19 on the journal or field, this added fewer citable documents to the Article
班级. The surprising number of books and book chapters (13% 全面的) seems to be primarily
due to pre-COVID-19 discussions about coronaviruses, matching the query “Coronavirus 2”.
The low number of conference proceedings may be due to conference cancellations, 或者
inability of most conferences to respond to the COVID-19 timescale.

Because the Dimensions type Article includes documents that would not be classed as stan-
dard journal articles in scientometric analyses, 这 295 Dimensions “Articles” from March 24
were visited to classify them by type. 仅有的 106 of these seemed to be standard journal articles.
The rest were mainly editorials, 字母 (called letters, letters to the editor, or correspondence;
one detailed letter was classed as an article), or news stories. 在某些情况下, documents were
called “article” by the publishing journal but were clearly news stories published in a news-
focused magazine/journal. The reduced set of 106 journal articles from March 24, 2020 曾是
used for follow-up correlation tests.

After Webometric Analyst had downloaded a complete set of records each day, 这
Mendeley API was used to identify the number of Mendeley readers for each document, 再次
using Webometric Analyst. It queries by DOI and by title/author/year and combines nonover-
lapping results for the most complete reader count. This follows best practice (Zahedi, Haustein,
& Bowman, 2014). Webometric Analyst was also used to identify counts of citations in Twitter,
Facebook, 红迪网, and online news outlets to these documents, as identified by DOI queries to
Altmetric.com. This data provider seems to have the most comprehensive coverage of Twitter,
the largest of the sources (Ortega, 2018A). Twitter and Facebook are logical choices to inves-
tigate because they seem to be the social media sources that most cite academic research
(Costas et al., 2015; Thelwall, Haustein, 等人。, 2013). Reddit and news may give a news per-
观望的, although Reddit is a multipurpose site (Ovadia, 2015; Stoddard, 2015) and the news
sources harvested by Altmetric.com presumably exclude some major paywalled press sources.

There were some gaps in the data collection due to documents not being returned by a query
on one day when they had been returned on a previous day. This produced missing citation and
altmetric scores, affecting the analysis. To avoid this issue, these missing values were replaced
with approximate values by linear interpolation (when scores were available for previous and
subsequent dates), linear extrapolation (when at least two previous but no subsequent scores
were available), or constant values (when only one previous value was available).

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COVID-19 publications

3.3. 分析

The coverage of the different sources was evaluated by comparing (on a graph) 的数量
query matches over time. This is not a fair comparison because the queries are not equivalent,
a researcher may use other queries, and the sources index with different levels of comprehensive-
内斯. 例如, a source that indexed the full text of documents would get more and probably
less relevant hits than a source indexing the title and abstract, even if they had the same coverage.

To assess the types of document generating the most impact for each source, the top 5 for each
indicator was extracted to give a manageable set. A comparison of the relative ranks of these
documents for the different indicators was used to guide the evaluation of the relative importance
of the document characteristics, along with the document age (younger documents would tend to
have lower scores in less rapidly evolving indicators). This focus on the highest scoring documents
seems reasonable because they are likely to be the most influential or important, 虽然
different trends may apply to more average documents.

To compare the average accumulation speed and scores of COVID-19 documents, a base set
was chosen, consisting of documents first indexed in Dimensions on March 24, 2020. 这是
date from the first week with the most new documents (excluding the first day). These documents
form a set that are likely to have been published on or shortly before March 24, 2020. The altmetric
and citation scores for this set were compared over time to assess their evolution and relative mag-
nitude. Averages were calculated with geometric means (与一个 +1 抵消: Fairclough & Thelwall,
2015) rather than arithmetic means due to the highly skewed nature of citations (de Solla Price,
1976; 华莱士, Larivière, & Gingras, 2009) and altmetrics (Thelwall & Wilson, 2016; 于, 徐, 等人。,
2017). The scores of this set were then compared using Spearman correlations to assess the extent
to which they may reflect similar types of impact (Sud & Thelwall, 2014). Because altmetrics other
than Mendeley tend to have very weak correlations with citation counts (Costas et al., 2015;
Haustein et al., 2014; Thelwall, Haustein, 等人。, 2013), high correlations are not expected.
Correlations were used rather than regression because this is the standard technique for altmetrics
and in this case matches the hypothesis’ use case: sorting documents matching COVID-19 queries
using altmetrics or citations.

Field normalization was not used for either analysis because (A) the papers cover a relatively
narrow topic (COVID-19) even though they span many subject areas and (乙) it is impractical to
field normalize the values because this would require daily updates of the whole of Dimensions,
Altmetric.com, and Mendeley for the world reference sets.

4. 结果

4.1. Coverage of Scholarly Databases

Based on the estimated number of manual search results returned by the sources queried, 它似乎
that Dimensions has substantially wider coverage of COVID-19 publications than all other sources
or finds more because of its full text indexing rather than just searching metadata (数字 2). 谷歌
Scholar probably indexes at least as many documents as Dimensions, although this could not be
checked because of the number of false matches it returned (a graph including Google Scholar is in
version 1 of this paper at https://arxiv.org/abs/2004.10400v1).

Google Scholar and Dimensions index both publisher records and other online publications
(preprint archives for Dimensions, wider web sources for Google Scholar). As Dimensions seems
able to identify COVID-19 publications more quickly or more widely than WoS and Scopus,
academics studying the area should consider Dimensions (or Google Scholar, if the false
matches are not a concern) if more specialist databases, such as PubMed, are not adequate.

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COVID-19 publications

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数字 2. The daily number of hits for COVID-19 queries (见表 1) from a range of scholarly
来源 (行进 22 to April 18).

This argument does not take into account the importance of the documents, 然而, 它是
possible that the key publications are quickly peer reviewed, published, and indexed by Scopus
and WoS. The Dimensions results include editorials, 消息, and letters, and may include recent
documents not about the disease but that mention it for background information in their full text.

Overlaps Between Dimension, Scopus and WoS

The extent of overlaps between the COVID-19 query results for Dimensions, Scopus, and WoS
were estimated on April 19, 2020 to assess whether they were indexing the same publications.
To obtain a relevant set of COVID-19 publications, only publications from 2019–2020 with
the terms “COVID” 或者 “coronavirus” 或者 “2019-nCoV” 或者 “SARS-CoV-2” 或者 “Corona” 在他们的
titles were selected. Publications with DOIs were matched between the three databases to
assess the percentage overlap between them (桌子 5). Few of the Dimensions publications
were also in Scopus (23.3%) or WoS (11.8%). Two-fifths (40.4%) of the Scopus publications
were in WoS and four-fifths (81.9%) of WoS publications were in Scopus. Google Scholar
could not be compared without a comprehensive list of search matches.

桌子 5. Overlaps for COVID-19 publications with DOIs between Dimension, Scopus, and Web of Science (四月 18, 2020)

Overlap % (No.)

Nonoverlapping % (No.)

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Scopus

WoS

2,166

1,067

Total publications
8,642

Scopus
23.3% (2,010)

–

WoS
11.8% (1,017)

40.4% (874)

Scopus
76.7% (6,632)

–

WoS
88.2% (7,624)

59.6% (1,292)

81.9% (874)

–

18.1% (193)

–

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COVID-19 publications

Publications in Dimensions but neither WoS nor Scopus were investigated to identify the doc-
ument types uniquely found by Dimensions. The Dimensions-only publications were almost all
从 2020 (95%), as were the publications that were also in Scopus or WoS (99%). The biggest
single source was preprint archives (39% of the documents unique to Dimensions): medRxiv,
SSRN, Research Square, bioRxiv, chemRxiv, and JMIR Preprints. An additional 2% were other
nonjournal publications (例如, book chapters). The remainder were either in journals not indexed
by WoS and Scopus or in journals indexed more slowly by WoS and Scopus. 例如,
Dimensions had indexed 58 Nature articles that Scopus and WoS had not yet recorded (虽然
they included 19 others in Nature), and neither Scopus nor WoS had indexed Medical Gas
Research or Chinese Journal of Internal Medicine.

In terms of citations found by the three databases for the matching publications, Dimensions
citation counts for all its matching COVID-19 publications were 4.9 和 2.8 times as numerous
as WoS and Scopus, suggesting that for recently published or in-press articles, Dimensions had
faster citation indexing than WoS and Scopus or from faster sources, such as preprint archives.
This could be important when scholars want to consider early citation impact evidence for
identifying relevant COVID-19 publications or for the impact assessment of published articles.

4.2. Most Cited Papers

Other factors being equal, the most cited papers are likely to be at the core of humanity’s early
response to COVID-19 and the most mentioned papers illustrate the public perception of the
most relevant research. The age, 类型, publication venue, and titles of these documents may
therefore give insights into important early scientific contributions to the disease. Lower ranked
documents are likely to have a different character, 然而, so the results should not be used
as proxies for all COVID-19 research.

The documents with the most Mendeley readers and Dimensions citations tended to be similar
and to provide primary clinical and epidemiological evidence about COVID-19 (桌子 6). Shorter
publication formats and analyses are more evident in the social web and news sources, 代表-
senting a partially different type of document. The social web and news articles also seemed to
give information that might be particularly useful, as public health information for the vast majority
of the planet’s population that had not yet caught COVID-19 by 18 四月 2020. 这些包括
studies on facemasks, the stability of the virus on surfaces, and pregnancy risks.

None of the five documents most cited on Reddit were also in the top five for the other sources,
although they seem to cover similar topics (桌子 7). The paper about Malayan pangolins is the
exception for not covering the primary characteristics of the disease or public health issues. 这
may be an artifact of the relatively low numbers of Reddit citations.

Although the top five articles for Dimensions were published in 2020, by March 21 they had all
been cited at least 200 times in Dimensions (数字 3), perhaps mainly by preprints, 字母, 和
short-form fast-publishing formats, such as brief communications, (academic) 消息, and case
报告. All five documents exhibit a reasonably steady rate of increase. The simultaneous jumps
in the lines presumably reflect weekly large-scale database refreshing for Dimensions, 虽然
there were also smaller daily changes.

The top five Mendeley documents also started March 21 with a high number of readers, 但
almost five times more than the number of Dimensions citations (数字 4). There was a similar
pattern of steadily increasing numbers of Mendeley readers with periodic interruptions. 在这种情况下
the interruptions resulted in temporary decreases in the numbers of Mendeley readers. This could
be due to two factors. Either the database consolidates weekly, such as by merging duplicates, 或者

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桌子 6. Characteristics and ranks of COVID-19 papers in the top five for Dimensions (D), Mendeley (中号), 推特 (时间), Facebook (F), and News
(氮), and their ranks in these sites on April 18, 2020. Citation and altmetric counts are in the figures below.

标题
Clinical features of patients infected with 2019 小说

Journal*

Lancet

日期
一月 24

Type

文章

D M T
1
1

氮

coronavirus in Wuhan, 中国

A novel coronavirus from patients with pneumonia

NEJM

二月 20

Brief report

in China, 2019

Early transmission dynamics in Wuhan, 中国, 的

NEJM

行进 26

文章

Novel coronavirus-Infected pneumonia

Epidemiological and clinical characteristics of

Lancet

一月 30

文章

99 cases of 2019 novel coronavirus pneumonia
in Wuhan, 中国: A descriptive study

Clinical characteristics of 138 hospitalized patients

JAMA

二月 7

Original

和 2019 novel Coronavirus-Infected pneumonia
in Wuhan, 中国

调查

Clinical characteristics of coronavirus disease 2019

NEJM

二月 28

文章

in China

Clinical course and risk factors for mortality of adult
inpatients with COVID-19 in Wuhan, 中国: A
retrospective cohort study

The proximal origin of SARS-CoV-2

Lancet

行进 11

文章

自然

药品

行进 17

Correspondence

Treatment of 5 critically ill patients with COVID-19

JAMA

行进 27

with convalescent plasma

初步的
通讯.

Respiratory virus shedding in exhaled breath and

自然

四月 3

Brief Comm.

efficacy of face masks

药品

Aerosol and surface stability of SARS-CoV-2 as

NEJM

行进 17

Correspondence

compared with SARS-CoV-1

Coronavirus latest: CERN scientists join the

自然

四月 8

消息

COVID-19 fight

Clinical characteristics and intrauterine vertical

Lancet

二月 12

文章

transmission potential of COVID-19 infection in
nine pregnant women: a retrospective review of
medical records

Characteristics of and important lessons from the
coronavirus disease 2019 (COVID-19) outbreak
in China

JAMA

二月 24

View-point

The incubation period of coronavirus disease 2019
(COVID-19) from publicly reported confirmed
案例: Estimation and application

Annals of
Internal
药品

行进 10

Original

研究

Severe outcomes among patients with coronavirus

疾病 2019 (COVID-19) – United States,
February 12–March 16, 2020

Morbidity

行进 18

报告

死亡
Weekly
报告

* NEJM: New England Journal of Medicine; JAMA: Journal of the American Medical Association.

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桌子 7. Characteristics and ranks of COVID-19 papers in the top five for Reddit, and their ranks in these sites on April 18, 2020. There is no
overlap with Table 1. Citation and altmetric counts are in the figures below.

标题
The neuroinvasive potential of SARS-CoV2 may play a role in the

杂志
Journal of Medical

日期
二月 27

Type
审查

respiratory failure of COVID-19 patients

Virology

Persistence of coronaviruses on inanimate surfaces and its inactivation

Journal of Hospital

二月 6

审查

with biocidal agents

感染

High temperature and high humidity reduce the transmission of COVID-19

SSRN

行进 10

Preprint

Identifying SARS-CoV-2 related coronaviruses in Malayan pangolins

自然

行进 26

文章

Early release – high contagiousness and rapid spread of severe acute

respiratory syndrome coronavirus 2 – Volume 26, Number 7-July 2020

Emerging Infectious

四月 7

研究

Diseases

右
1

its search is somehow weakened periodically so that the free text search (which is submitted in
parallel with the DOI search) matches fewer documents. It is not possible to check which is correct
from the data because Mendeley reports reader counts, not the identities of these readers.

推特 (数字 5) has a very different pattern to Dimensions and Mendeley. 第一的, 一些
the documents are much younger, published during the date range analyzed. 第二, 这
number of tweeters achieves close to its maximum when first found by Dimensions, 虽然
this is not necessarily the original publication date.

Facebook has a similar growth pattern to Twitter, except that there is a period of increasing
interest for the proximal origin paper (数字 6), which has a more moderate growth on Twitter.
一个 (apparently speculative) news story about CERN scientists that was popular on Facebook
did not get traction on Twitter and seems unlikely to become highly cited or read.

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数字 3. The cumulative number of Dimensions citations for the five most cited COVID-19
文件.

Quantitative Science Studies

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COVID-19 publications

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数字 4. The cumulative number of Mendeley readers for the five most read COVID-19 documents.

The top news-cited articles were all covered by at least 400 news sources by the end of the
时期 (数字 7). 也许令人惊讶, given that news is very time-dependant, all the sources
experienced significant increases in the number of citing sources. Altmetric.com is constantly
expanding its coverage of news sources (这是可能的, but seems unlikely), it is slow to

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数字 5. The cumulative number of Tweeters for the five most tweeted COVID-19 documents.

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COVID-19 publications

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数字 6. The cumulative number of Facebook wall posts for the five most walled COVID-19 documents.

update its news coverage, or news stories about COVID-19 are prepared to cite old articles, 每-
haps for a more in-depth commentary or as background context for new articles.

There were relatively few citations from Reddit, despite its use as a news source and many
academic themes (subreddits) within the site (数字 8). Perhaps reflecting its news status, 较老的
articles do not seem to increase their Reddit citation counts.

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数字 7. The number of news citations for the five most News-cited COVID-19 documents.

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COVID-19 publications

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数字 8. The cumulative number of Reddit posts for the five most posted COVID-19 documents.

4.3. A Comparison Between Average Scores for Different Sources

行进 24, 2020 was selected for a time series analysis because this date in the first week had
the most new articles (349) found by Dimensions. For documents first found by Dimensions on
行进 24, 2020 and matching the COVID-19 queries, the average score was highest for
Twitter and already above 1 on the start day (数字 9). Average tweeter counts then increased
slowly after the first few days. 相比之下, average Mendeley reader counts for these 349 阿尔-
ticles started close to zero and increased rapidly, except for weekly Mendeley indexing adjust-
评论. Mendeley overtook Twitter after a week.

The low initial value for Dimensions citations and the high initial average number of tweets
are unsurprising, given that citations take time to accrue due to publication delays (even for
preprints), but articles can be tweeted as soon as they are published. As Twitter is a news source
and authors/editors/publishers/current awareness browsers might tweet to announce a publica-
的, high initial tweet counts are to be expected. Mendeley users can also add papers to their
libraries as soon as they are published, but the slow growth might represent researchers and
students saving the articles to read on the day of publication and then adding them to
Mendeley after reading them. The figures for Mendeley are likely to also include people that
found the articles through literature searches rather than current awareness, adding them to
Mendeley when read, 成立, or cited in a paper (Mendeley automatically builds reference lists).

The average citation counts for the remaining three sources were all much lower than for
Mendeley and Twitter (数字 10). Although Facebook and Reddit both displayed a similar
growth pattern to Twitter (rapid initially, then slow), both News citations and Dimensions ci-
tations increased steadily. The low number of Dimensions citations is unsurprising, given pub-
lication delays, and the small but nontrivial increase for Dimensions suggests that many
authors of COVID-19 papers quickly found current research and added it to their papers, 然后
published them as preprints. The constant growth for news sources is unexpected, given that
news is supposed to be current. Possible explanations for this (in addition to those discussed

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数字 9. Daily average (geometric mean) citations by source for documents first found by
Dimensions on March 24, 2020 (n = 349).

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数字 10. Daily average (geometric mean) citations by source for documents first found by
Dimensions on March 24, 2020, excluding Mendeley and Twitter (n = 349).

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多于) are delays in the production of slower news sources (例如, magazine-type articles), delays
in writing university press releases, and articles being discussed in the press after passing peer
review or after being formally published in early view or an online volume.

4.4. Overlaps in Citation Counts Between Sources

Spearman correlation tests reveal the extent to which the same documents that are cited by one
source are also cited by another source, together with the extent that they are cited. By April 18,
2020, correlations between Dimensions citations and altmetrics for documents first found by
Dimensions on March 24 were strong, except for Reddit (桌子 7). Because most (229; 66%)
documents were uncited by April 18, the correlation mainly confirms that, except for Reddit,
news stories, publishing authors, and users of the different platforms tended to select the same
documents for attention. The altmetrics also correlated moderately or strongly with each other,
except for Reddit, in agreement with this conclusion. 因此, for the narrow topic of COVID-19,
there seems to be a researcher-news-social media consensus about the most important topics,
at least in the (非常) 短期.

The correlations (桌子 8) do not take into account field differences or document type differ-
恩塞斯. The relatively high correlations could be at least partially due to ignoring contributions of
low relevance to COVID-19, such as book chapters mentioning the possibility of a coronavirus 2,
editorials, 字母, and subject areas making relatively peripheral contributions to immediate
需要.

The positive correlations might be influenced by a mix of publication venues and document
类型. 这 349 documents included 239 (68.5%) papers in journals, 67 (19.2%) papers in pre-
print archives, 和 27 (7.7%) magazine articles, 和 16 (4.6%) not assigned to a publication
venue by Dimensions (例如, book chapters, 报告). In terms of rank order, for all five sources,
一般, journal articles were more highly ranked than the other types and preprints were
more highly, or equally ranked with, magazine articles. The average ranks were Journals
(D: 158; 中号: 136; 时间: 154; F: 168; 氮: 165; 右: 167); preprints (D: 198; 中号: 258; 时间: 195; F: 191;
氮: 186; 右: 191.5); 杂志 (D: 235; 中号: 258; 时间: 280; F: 191; 氮: 221; 右: 191.5). The magazines
(Alcoholism & Drug Abuse Weekly; Focus on Catalysts) included news stories about the societal
side-effects of the disease rather than research about the disease (例如, “China refineries reduce
operating rates”). Preprints presumably attract less attention because they have not been peer
reviewed. 此外, the documents in journals included letters and news stories, 这可能
also have lower relevance to COVID-19 research and many received little attention from any

桌子 8. Spearman correlations between citation counts and altmetrics from April 18, 2020 为了
COVID-19 documents first found by Dimensions on March 24, 2020. All are statistically significant
at p = .001 (n = 349)

Dimensions

Mendeley

推特

Facebook

消息

Mendeley
.653*

推特
.659*

.689*

Facebook
.453*

.375*

.411*

消息
.529*

.473*

.626*

.376*

* Statistically significant at p = 0.001.

红迪网
.249*

.354*

.363*

.251*

.335*

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来源 (例如, the uncited news article “Seven days in medicine: 11–17 March 2020”, in the BMJ,
和 27 readers and two tweets). 因此, both altmetrics and citations seem to focus on contribu-
tions of types that are more core to COVID-19 as a medical and public health research issue.

The influence of nonarticle document types on the correlations were tested by filtering out
all nonarticles. After manually removing documents that were not journal articles (mainly ed-
itorials, 消息, and letters), there were 106 standard journal articles (including reviews).
尽管如此, the correlations did not substantially change (桌子 9). Some of the removed
editorials had been cited, read, and shared, explaining the similar positive correlations.

The two most common Dimensions subject codes for the March 24 set were 1117 民众
Health and Health Services (n = 78) 和 1103 Clinical Sciences (n = 32). Except for Reddit
(correlations close to 0), the pairwise correlations change little if the set is restricted to only
subject categories 1117 或者 1103, with or without excluding nonarticle types. 例如, 这
lowest correlation between Twitter and Dimensions for any of these four restricted sets is .638
(类别 1103 with all document types, n = 32). 因此, except for Reddit, the strong positive
correlations between indicators do not seem to be due to field differences in the data set.

Focusing on the smallest data set mentioned above, 1103 Clinical Sciences, journal articles
仅有的 (n = 19), one of the reasons for the strong positive correlations is that three of the articles
were in national journals (Korean Journal of Radiology, Chung-Hua Wai Ko Tsa Chih, Chinese
Journal of Gastrointestinal Surgery) and the rest were in international journals or a prestigious
national journal that is effectively international ( JAMA). The three national articles collectively
had one citation, three tweets, no mentions in the other altmetrics, and two of the three lowest
Mendeley reader counts. This is consistent with lower quality or impact national research be-
ing less cited and less read, which is unsurprising. It is more surprising that national research is
less tweeted than international research, which has not previously been found by altmetrics
学习. 在这种情况下, two articles were not in English and this, combined with Twitter not being
used in China, might be the explanation.

The top article for all metrics in the 1103 Clinical Sciences journal articles only set (32
citations, 604 读者, 1504 tweeters, three Facebook walls, 31 news stories, one Reddit)
was the research letter (classified here as an article) “Characteristics and Outcomes of 21
Critically Ill Patients With COVID-19 in Washington State” from JAMA (published March
19, 2020, but picked up by Dimensions on March 23/24). This seems similar to the Annals
of Palliative Medicine article, “Risk factors associated with disease progression in a cohort of
[17] patients infected with the 2019 novel coronavirus” from March 22, 2020, which had low
scores on all metrics (zero citations, 92 (third fewest) Mendeley readers, one tweet, zero on the

桌子 9. Spearman correlations between citation counts and altmetrics from April 18, 2020 为了
COVID-19 journal articles first found by Dimensions on March 24, 2020 (n = 106)

Dimensions

Mendeley

推特

Facebook

消息

Mendeley
.693***

推特
.734***

.687***

Facebook
.589***

.401***

.562***

消息
.585***

.473***

.719***

.440***

红迪网
.250**

.316***

.382***

.215*

.334***

*Statistically significant at p = 0.05; **Statistically significant at p = 0.01; ***Statistically significant at p = 0.001.

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数字 11. Spearman correlations between altmetrics and Dimensions citation counts from April
18 for COVID-19 documents first found by Dimensions on March 24, 2020 (n = 349). Correlations
with Dimensions citation counts on the same dates are also reported for context.

其他的). The first article was in a more prestigious journal and concerned patients from the United
状态, whereas the second article was more detailed (例如, pictures, full article, more words,
statistical analysis, more references) and was about patients from Nanchang, 中国. 这, com-
bined with the previous three cases, suggests that the regional bias of Twitter (a natural side-
effect of news focusing on local issues) coincides with the US/UK or Western domination of
more prestigious medical journals. This might not have been visible previously in altmetric stud-
ies for the medical domain because the current data set presumably has a higher proportion of
Chinese articles than normal, given China’s earlier research into the disease. This is a speculative
结论, 然而, and may not be correct. Not all research from China or in nonprestigious
sources was ignored in academia. The second most cited article (in an apparently nonprestigious
international journal) 曾是, “Clinical features of severe pediatric patients with coronavirus dis-
舒适 2019 in Wuhan: a single center’s observational study” from the World Journal of Pediatrics
(seven citations, 347 读者 (second highest), 仅有的 32 tweets, zero others). This article’s focus
on children may have been relatively unique, and therefore particularly valuable for researchers.

Also for the same set of 19 Clinical Sciences articles from March 24, there seemed to be a
tendency for articles attracting more attention to be more central to COVID-19. Ignoring the
four articles discussed above, the remaining uncited article, also with low social media scores,
was “Coronavirus Disease (COVID-19): Spectrum of CT Findings and Temporal Progression of
the Disease,” from Academic Radiology, which focuses on the radiology dimension. 其他
relatively specialist and universally low scoring article was, “COVID-19 – what should anae-
thesiologists and intensivists know about it?” from Anaesthesiology Intensive Therapy (1 cita-
的, 218 读者, 18 tweets, 0 其他的).

4.5. Early Altmetrics and Later Citation Counts

理想情况下, an indicator would help researchers and policy-makers to identify important articles
when they are first published, without having to wait for enough citations. To check for early
evidence of later citation impact, the indicators were correlated with Dimensions citation

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counts on April 18, representing longer term citation counts (this is a weak proxy, 因为
decades are sometimes used for long-term citations in other contexts, such as Stegehuis,
Litvak, & Waltman, 2015).

On the day that a document is first findable in Dimensions, its tweeter count is the best
indicator of likely long-term citation impact (数字 11). Twitter users seem to be able to notice
documents approximately on the date of first publication for their potential importance to
COVID-19. After this date, the tweeter count does not increase much and its correlation with
longer term Dimensions citations is stable. After about 3 weeks, Mendeley reader counts take
over as a marginally better indicator of longer term citation impact. It is not clear whether the
same would be true for more mature citation counts, 然而, such as after a year. 有可能的
that early Dimensions citations (and Mendeley readers) reflect more temporary interest and are
themselves highly influenced by the news or social sharing on Twitter, 例如. The most
cited sets of five papers analyzed above suggest that highly recognized papers are particularly
important for the disease, 然而. As above, this correlation ignores field differences and
document type differences, although document differences seem to have little effect (Tables 8
和 9).

5. 讨论

The results are limited by the range of factors mentioned in Section 3. 尤其, the coverage
figures for the sources are not directly comparable due to the different scopes of the queries. 在
添加, the count data has not been field-normalized, so the coverage comparisons do not
reveal disciplinary differences. The correlations may also be exaggerated by not taking into
account disciplinary differences. The results may show different patterns for earlier or later time
periods. The properties of the scholarly databases and Altmetric.com’s strategies may evolve
随着时间的推移, rendering the results obsolete. They may also not be applicable for later stages of
COVID-19 research or for future epidemics or pandemics.

The COVID-19 query results comparison confirms the previous finding that COVID-related
academic publications are appearing rapidly (Torres-Salinas, 2020). 此外, it confirms that
Dimensions finds many publications not in Scopus and WoS but that Scopus indexes nearly all
relevant publications found in the WoS core collection with the Conference Proceedings
Citation Index. Presumably the difference would be smaller if other parts of WoS were included,
such as the Book Citation Index, although the core collection includes the Emerging Sources
Citation Index (Clarivate, 2020).

The results are not directly comparable to studies from before COVID-19 due to the unprec-
edented speed and volume of publishing on the topic. 例如, Dimensions citation counts
accrue more rapidly than previously reported for any topic. 用于比较, the Scopus citations
的 12 subject categories (full journal articles only) were a maximum of 0.12 in the month of
出版物, whereas the COVID-19 mixed set averaged almost double this after 3 weeks.
The results are also qualitatively different in some respects. Although correlation tests have
previously found tweeter counts to have little value as a scholarly impact indicator due to very
low correlation with citation counts, typically close to 0 or even negative (Costas et al., 2015;
Haustein et al., 2014; Thelwall, Haustein, 等人。, 2013), the current study has found tweet counts
to be reasonable academic impact indicators and the best early impact indicator for the first
3 weeks. This may be partly due to the set of articles here covering multiple disciplines, 但是
results for the top-cited documents suggest that altmetrics are effective at pointing to the docu-
ments that are most central to COVID-19 as a medical and public health issue. 因此, the unprec-
edented threat of COVID-19 seems to have led to an unprecedentedly high and focused level of

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societal and academic attention being given to the most relevant research. There was some sup-
port for this from the correlation analysis for March 24, 2020 文件. This correlation anal-
ysis also suggested that the high correlation may also be at least partly due to an international
问题: a relatively high amount of publishing from China not in prestigious journals coupled with
greater interest in research concerning patients in Twitter-using countries (特别是
美国), and that research sometimes being published in more prestigious journals.

6. 结论

The confirmed rapid increase in COVID-19 academic publications is encouraging in terms of
the academic community rapidly reacting to the need for relevant research and commentaries.
The importance of short-form and quick contributions (viewpoints, correspondence, 简短的
报告) is also evident in the highly cited papers, as is the importance of academic research
for practical public health issues. Dimensions seems to be the most comprehensive database
to find relevant literature, although Google Scholar might have wider coverage and be useful
to those that do not mind its false matches.

Despite the apparent high medical and public health value of some academic papers, the huge
number of publications returned by a relevant search will presumably make the most important
publications more difficult to find. This should not be a problem for medical researchers trained to
use MeSH queries effectively, but might be problematic for other researchers, end users, 和
民众, who may find bewilderingly many matches for their queries. The altmetric results suggest
that altmetrics may be helpful for researchers needed to quickly identify the most useful new
documents from the large number published daily. Altmetric counts may help to distinguish
between core primary research and other contributions, such as editorial commentaries with
narrower disciplinary or professional relevance (例如, radiographers). Perhaps ironically, given that
a core original goal for altmetrics was to develop indicators of societal impact that were different
from scholarly impact indicators (Priem et al., 2010), their greatest value (as early impact indica-
托尔斯) seems be occurring when the two concepts are most closely converging.

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作者贡献

Kayvan Kousha: 概念化, 数据管理, 调查, 方法, 可视化,
Writing—original draft, Writing—review & 编辑. Mike Thelwall: 概念化, Data cura-
的, 调查, 方法, 软件, 可视化, Writing—original draft, Writing—review
& 编辑.

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COMPETING INTERESTS

The authors have no competing interests.

资金信息

This research was not funded.

DATA AVAILABILITY

The processed data used to produce the graphs are available in the supplementary material
(https://doi.org/10.6084/m9.figshare.12301475).

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preparedness in response to novel coronavirus. Journal of the
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e0197265.

Mohammadi, E., Thelwall, M。, Haustein, S。, & Larivière, V. (2015).
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COVID-19 publications

Robinson-García, N。, Torres-Salinas, D ., Zahedi, Z。, & Costas, 右.
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Altmetric.com. El Profesional de la Información, 23(4), 359–366.
歌曲, P。, & Karako, 时间. (2020). COVID-19: Real-time dissemination
of scientific information to fight a public health emergency of
international concern. BioScience Trends, 14(1). https://doi.org/
10.5582/BST.2020.01056

Stegehuis, C。, Litvak, N。, & Waltman, L. (2015). Predicting the long-
term citation impact of recent publications. Journal of infor-
指标, 9(3), 642–657.

Stoddard, G. (2015). Popularity and quality in social news aggrega-
托尔斯: A study of Reddit and Hacker News. 在诉讼程序中
24th International Conference on World Wide Web (PP. 815–818).
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Sud, P。, & Thelwall, 中号. (2014). Evaluating altmetrics. Scientometrics,

98(2), 1131–1143.

Thelwall, 中号. (2017A). Are Mendeley reader counts useful impact
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Thelwall, 中号. (2017C). Why do papers have many Mendeley readers
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Thelwall, 中号. (2018A). Early Mendeley readers correlate with later

citation counts. Scientometrics, 115(3), 1231–1240.

Thelwall, 中号. (2018乙). Dimensions: A competitor to Scopus and the

Web of Science? Journal of Informetrics, 12(2), 430–435.

Thelwall, M。, Tsou, A。, Weingart, S。, Holmberg, K., & Haustein, S.
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485–495.

Torres-Salinas, D. (2020). Ritmo de crecimiento diario de la
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Zahedi, Z。, & Haustein, S. (2018). On the relationships between
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