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      Association Between Immediacy of Citations and Altmetrics in COVID-19 Research by Artificial Neural Networks

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          Abstract

          Objectives:

          Both citations and Altmetrics are indexes of influence of a publication, potentially useful, but to what extent that the professional-academic citation and media-dominated Altmetrics are consistent with each other is a topic worthy of being investigated. The objective is to show their correlation.

          Methods:

          DOI and citation information of coronavirus disease 2019 (COVID-19) researches were obtained from the Web of Science, its Altmetric indicators were collected from the Altmetrics. Correlation between the immediacy of citation and Altmetrics of COVID-19 research was studied by artificial neural networks.

          Results:

          Pearson coefficients are 0.962, 0.254, 0.222, 0.239, 0.363, 0.218, 0.136, 0.134, and 0.505 ( P < 0.01) for dimensions citation, attention score, journal impact factor, news, blogs, Twitter, Facebook, video, and Mendeley correlated with the SCI citation, respectively. The citations from the Web of Science and that from the Altmetrics have deviance large enough in the current. Altmetric score is not precise to describe the immediacy of citations of academic publication in COVID-19 research.

          Conclusions:

          The effects of news, blogs, Twitter, Facebook, video, and Mendeley on SCI citations are similar to that of the journal impact factor. This paper performs a pioneer study for investigating the role of academic topics across Altmetric sources on the dissemination of scholarly publications.

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          Is Open Access

          Can Tweets Predict Citations? Metrics of Social Impact Based on Twitter and Correlation with Traditional Metrics of Scientific Impact

          Gunther Eysenbach (2011)
          Background Citations in peer-reviewed articles and the impact factor are generally accepted measures of scientific impact. Web 2.0 tools such as Twitter, blogs or social bookmarking tools provide the possibility to construct innovative article-level or journal-level metrics to gauge impact and influence. However, the relationship of the these new metrics to traditional metrics such as citations is not known. Objective (1) To explore the feasibility of measuring social impact of and public attention to scholarly articles by analyzing buzz in social media, (2) to explore the dynamics, content, and timing of tweets relative to the publication of a scholarly article, and (3) to explore whether these metrics are sensitive and specific enough to predict highly cited articles. Methods Between July 2008 and November 2011, all tweets containing links to articles in the Journal of Medical Internet Research (JMIR) were mined. For a subset of 1573 tweets about 55 articles published between issues 3/2009 and 2/2010, different metrics of social media impact were calculated and compared against subsequent citation data from Scopus and Google Scholar 17 to 29 months later. A heuristic to predict the top-cited articles in each issue through tweet metrics was validated. Results A total of 4208 tweets cited 286 distinct JMIR articles. The distribution of tweets over the first 30 days after article publication followed a power law (Zipf, Bradford, or Pareto distribution), with most tweets sent on the day when an article was published (1458/3318, 43.94% of all tweets in a 60-day period) or on the following day (528/3318, 15.9%), followed by a rapid decay. The Pearson correlations between tweetations and citations were moderate and statistically significant, with correlation coefficients ranging from .42 to .72 for the log-transformed Google Scholar citations, but were less clear for Scopus citations and rank correlations. A linear multivariate model with time and tweets as significant predictors (P < .001) could explain 27% of the variation of citations. Highly tweeted articles were 11 times more likely to be highly cited than less-tweeted articles (9/12 or 75% of highly tweeted article were highly cited, while only 3/43 or 7% of less-tweeted articles were highly cited; rate ratio 0.75/0.07 = 10.75, 95% confidence interval, 3.4–33.6). Top-cited articles can be predicted from top-tweeted articles with 93% specificity and 75% sensitivity. Conclusions Tweets can predict highly cited articles within the first 3 days of article publication. Social media activity either increases citations or reflects the underlying qualities of the article that also predict citations, but the true use of these metrics is to measure the distinct concept of social impact. Social impact measures based on tweets are proposed to complement traditional citation metrics. The proposed twimpact factor may be a useful and timely metric to measure uptake of research findings and to filter research findings resonating with the public in real time.
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            Artificial neural networks: fundamentals, computing, design, and application

            I.A Basheer, M Hajmeer (2000)
            Journal of Microbiological Methods, 43(1), 3-31
            Article 0 comments Cited 231 times – based on 0 reviews     Review now
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              A Principal Component Analysis of 39 Scientific Impact Measures

              Johan Bollen, Herbert Van de Sompel, Aric Hagberg … (2009)
              Background The impact of scientific publications has traditionally been expressed in terms of citation counts. However, scientific activity has moved online over the past decade. To better capture scientific impact in the digital era, a variety of new impact measures has been proposed on the basis of social network analysis and usage log data. Here we investigate how these new measures relate to each other, and how accurately and completely they express scientific impact. Methodology We performed a principal component analysis of the rankings produced by 39 existing and proposed measures of scholarly impact that were calculated on the basis of both citation and usage log data. Conclusions Our results indicate that the notion of scientific impact is a multi-dimensional construct that can not be adequately measured by any single indicator, although some measures are more suitable than others. The commonly used citation Impact Factor is not positioned at the core of this construct, but at its periphery, and should thus be used with caution.
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                Author and article information

                Journal
                Journal ID (nlm-ta): Disaster Med Public Health Prep
                Journal ID (iso-abbrev): Disaster Med Public Health Prep
                Journal ID (publisher-id): DMP
                Title: Disaster Medicine and Public Health Preparedness
                Publisher: Cambridge University Press (New York, USA )
                ISSN (Print): 1935-7893
                ISSN (Electronic): 1938-744X
                Publication date (Electronic, pub): 31 August 2021
                Pages: 1-6
                Affiliations
                Library, University of Science and Technology Beijing , Beijing, P.R. China
                Author notes
                Corresponding author: Na Ran, Email: ranna@ 123456ustb.edu.cn .
                Author information
                https://orcid.org/0000-0003-2204-2183
                Article
                Publisher-manuscript ID: DMPHP-20-2842 Publisher Item ID: S1935789321002779
                DOI: 10.1017/dmp.2021.277
                PMC ID: 8505816
                PubMed ID: 34462034
                SO-VID: c08b2cb8-e744-464c-a2c5-f661bdd975ae
                Copyright © © Society for Disaster Medicine and Public Health, Inc. 2021
                License:

                This article is made available via the PMC Open Access Subset for unrestricted re-use and analyses in any form or by any means subject to acknowledgement of the original source. These permissions are granted for the duration of the COVID-19 pandemic or until permissions are revoked in writing. Upon expiration of these permissions, PMC is granted a perpetual license to make this article available via PMC and Europe PMC, consistent with existing copyright protections.

                History
                Date received : 11 December 2020
                Date revision received : 06 May 2021
                Date accepted : 22 August 2021
                Page count
                Figures: 4, Tables: 3, Equations: 1, References: 39, Pages: 6
                Categories
                Subject: Original Research

                Keywords: journal impact factor,social media,covid-19,scholarly communication,neural networks
                Data availability:
                Keywords: journal impact factor, social media, covid-19, scholarly communication, neural networks

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