Original Article

An Altmetric Analysis of Top 100 Cited Articles on Perinatal Infection


  • Deniz BORCAK
  • Hatice BULUT

Received Date: 26.04.2022 Accepted Date: 09.09.2022 Bezmialem Science 2023;11(1):40-52


Pregnant women and their fetuses are at increased risk of complications of viral, bacterial, and parasitic infections. For most infections, effective preventive strategies are available. Scientific studies on perinatal infections show advances in this field. The primary objective of this study was to evaluate the social attention paid to highly cited articles on perinatal infection in the last decade. Factors of altmetrics performance, including twitter mentions and the correlation between altmetrics and traditional citation counts were analyzed.


We created the 100 top-cited articles (T100) list from the Web of Science database and altmetric.com website among 4,240 perinatal infection articles.


The most cited article “Clinical analysis of 10 neonates born to mothers with 2019-nCoV pneumonia” by Zhu H. published in the Translational Pediatrics Journal. The T100 list included 75 original scientific research publications and 25 review articles. On Twitter, 80 of the T100 articles were shared. Of the ten most tweeted articles, five were about 2019-nCoV, four were about Hepatitis B virus, and one was about Zikavirus. The number of AAS, average citations, and the number of tweets (NT) increased statistically significantly as the years increased. A statistically significant and strong correlation was found between AAS and the number of tweets.


This study reflects the most influential publications to identify the trends of current studies and provides some directions for future studies to help researchers. Also, it presents a view on the subject of the level of interest shown by the scientific world on social media platforms to the most cited articles on the subject of perinatal infection.

Keywords: Perinatal infection, altmetric, social media, top-cited article, twitter


Infections during pregnancy are more common and complicated due to immigration, international travel, increasing viral morbidity, reduced primary and annual influenza vaccination (1,2). It is possible to prevent perinatal infections by implementing adequate strategies, more sensitive diagnostic techniques, and postnatal retrospective screening programs on time (3).

Citations are the basis for metrics like the h-index and its derivatives, which are used to evaluate the productivity and impact of individual researchers or the impact factor (IF) which is used to evaluate the scientific impact of journals (4-6). Traditionally, assessing the quality of publications based on the number of citations does not precisely reflect the quality (7,8). Bibliometric analysis is a statistical evaluation of scientific publications and provides an effective method to measure and compare the scientific value and impact of articles using a quantitative appraisal of citations, articles, and journals (9,10).  A new score called Altmetric Attention Score (AAS) was created to measure the impact of scientific articles on social media (11). The AAS is qualitative data that is complementary to traditional citation-based metrics (12). However, in comparison to traditional citation metrics, altmetrics measure the impact of an article after publication, based on its number of mentions across various online sources. The article’s final AAS reflects the summation of these weighted mentions (13). The AAS and the altmetric donut were developed to measure how much and what kind of attention a study received. The color of the source that gives the highest score to the research takes up more place in the donut. Each color on the altmetric donut symbolizes a different source of attention and the altmetric score is written in the donut’s center (Figure 1). Social media attention following the publication of an article has previously been shown to correlate with the subsequent citation rate (14,15). Medical journals use social media, including blogs and commercial platforms such as Facebook and Twitter, to share medical information (16,17).

The objective of this study is to provide bibliometric and altmetric overviews and visualizations of the perinatal infection research, as well as to evaluate the association between traditional bibliometric analysis and altmetric analysis. In addition, we aimed to analyze the impact of Twitter on both metrics in terms of scientific knowledge dissemination.


Study Design

Our research was a retrospective clinical investigation with a level of evidence of three or group B based on the Scottish Intercollegiate Guidelines Network (SIGN) (18). The “perinatal infection” keyword was used in the Web of Science (WoS) Core Collection database (Philadelphia, Pennsylvania, United States) to find the articles (date of access: April 12, 2021)  between 2011 and 2021. No language restrictions were set. The data was entered and analyzed using Microsoft Excel files. The IF’s of journals were recorded based on the 2019 Clarivate Journal Citation Reports. The quartile (Q) scores and H-index of journals were determined using the 2020 Scimago Journal and Country Rank (19). Study types and levels of evidence were determined using SIGN 100.  The bibliometric data of the T100 list (Table 1) was visualized using the VOSviewer software version 1.6.16 (20). The findings of country coupling and keyword co-occurrence analyses were visualized on maps. Altmetric attention scores were obtained by downloading the “Altmetric it” function from the Altmetric.com website (21-24). The website created AAS automatically using a mechanism based on a weighted average of each article’s attention.  Additionally, we determined how many times each article was shared on Twitter.

Each author certified that the study was conducted following the ethical principles of the Helsinki Declaration. This study did not require ethical approval as it performed bibliometric and altmetric analysis of currently published articles on perinatal infections.

Statistical Analysis

All analyses were performed by IBM SPSS for windows version 23.0 (SPSS Inc., Chicago, IL, USA). Continuous variables were described using the median and interquartile range (IQRs), whereas categorical variables were defined using percentages.  The comparative analysis of the parameter values according to the publication years and main subject were made with the Kruskal-Wallis test and the post hoc tests were made with the Dunn test. Mann-Whitney U test was used for comparisons based on Q categories. The Shapiro-Wilk test was used to determine the parameters’ conformity to the normal distribution. Sperman or Pearson correlation coefficients were calculated to detect the linear relationship between numerical variables. Beta coefficients were estimated by univariate linear regression analysis. P<0.05 was considered statistically significant.


Using the term “infection” in the WoS search, we found 852,880 publications between 2011 and 2021. Later, the term “perinatal” was added and the total number of publications decreased to 4240. All of the articles on the T100 list were published in English.

Total Citation Number (TCN) and AAS Analysis

The median values for TCN and AAS scores were 4.5 (IQR 43-477) and 172 (IQR 0-1307), respectively in the T100 list. The citation number was ranked between 477 and 43. The T100 list was ranked between AAS 1,377 and 0. There were ten articles on the T100 list that did not have AAS yet. The most cited article was published by Zhu et al. (25) in April 2020 and received a TCN of 477 and AAS of 442 at the Translational Pediatrics journal. The article by Allotey et al. (26) had the highest value with 1307 AAS on the T100 list and it was published in British Medical Journal. The four of the articles with the highest AAS were among the top 10 articles with the highest CN.

Twitter Analysis

On Twitter, 80 articles from the T100 list were found to be shared. Of the ten most tweeted articles, five were about 2019-nCoV, four were about Hepatitis B virus (HBV), and one was about Zikavirus. The most-tweeted article was “Maternal and perinatal outcomes with COVID-19: A systematic review of 108 pregnancies” with 762 retweets (27).

Journal Perspective

The T100 articles were published in 59 journals with the number of articles per journal ranging from 1 to 8. Pediatrics, Clinical Infectious Diseases, and Journal of Hepatology were the three leading journals in which the majority of the articles were published. The most cited journal was Translational Pediatrics with 477 citations, followed by Eurosurveillance with 412 citations and New England Journal of Medicine with 258 citations.  The most tweeted articles were published in Acta Obstetrıcıa et Gynecologıca Scandinavica, British Medical Journal and Archives of Pathology & Laboratory Medicine, respectively. According to SJCR, all journals received Q1 scores, except for five journals that received Q2 scores.

Article Types

The 75 articles on the T100 list were original scientific researches and 25 articles were review articles (Table 2). Also, there were 16 meta-analyses and 5 randomized controlled trials. The AAS values of studies with evidence level 1 were found to be statistically significantly higher than those with level 3 (p=0.024). Studies with evidence level 1 had statistically substantially higher average and total citation values than studies with evidence levels 2 and 3 (p<0.05).

Research Topics

When we evaluated the T100 list in terms of the main topic, the majority of articles were related to risk factors for transmission (n=23), epidemiology (n=19), prevention (n=18), clinical features, and outcomes (n=16) of perinatal infections (Table 3). The most frequent microorganisms were HBV, followed by human immunodeficiency virus (HIV) and 2019-nCoV (Table 4).

Distribution of Countries

The 100 top-cited perinatal infections articles were from 41 countries. The United States had the highest total number of publications (59), followed by China (16), England (10), Switzerland and South Africa (8), France (7) and Australia (6).  The country with the most citations was the USA with 5193 citations, followed by China with 1997 citations and France with 957 citations.

Correlation Analysis

The number of AAS, average citations, and the number of tweets  increased statistically significantly as the years increased (p<0.001). The AAS numbers, total citation number, the average citation, and tweet counts of studies with main subjects “clinical features” and “outcomes” were found to be statistically significantly higher than studies on other subjects (p<0.05). A statistically significant and strong correlation was found between AAS and the number of tweets (r=0.808, p=0.001).  As the number of tweets got higher, the AAS was likely to increase as a consequence. There was a positive correlation between AAS and the average per year, and a negative statistically significant moderate correlation between AAS and the variable of the number of years since publication (p<0.05). A statistically significant weak correlation was found between the journal IF and total citation numbers (r=0.209, p=0.040). A statistically significant and strong correlation was found between the journal IF and the h index (r=0.840, p=0.001). Positive and statistically significant weak correlations were found between the Q category, total citation number and average per year (p<0.05).

The correlation between AAS, TCN, NYsP, and journal IF, H-index, and Q categories are shown in Table 5. There was a strong positive correlation between AAS and the average per year (r=0.430; p=0.001) and there was a strong positive correlation between the average of per year and the number of tweets (r=0.461; p=0.001) (Figure 2). 

Visualization Analysis

For each of the 41 countries on the T100 list, the total strength of bibliographic coupling linkages with other countries was measured and visualized (Figure 3).  Large nodes refer to countries that are productive and efficient. The degree of communication and collaboration across nations is shown by the thickness and distance of linkages between nodes (28). United States Department of Health Human Servıces (28), National Institutes of Health Nih USA (25), and Nih National Institute of Allergy Infectious Diseases Niaid (13) were the leading institutions. Co-occurrence analysis of high-frequency keywords was performed. The minimal number of keyword co-occurrences criteria was chosen to be 2. The criteria were met by 25 of the 43 retrieved keywords related to perinatal infection.  The network was used to cluster related keywords, and the five major clusters were represented by the colors, red, green, blue, yellow, and purple, respectively (Figure 4). “Pregnancy” and “perinatal transmission” were the most frequently used keywords.



n previous studies, AAS was detected at different ranges. Moon et al. (29) found the AAS between 7,301-34,789 in their study.  Li et al. (30) stated AAS values between 57 and 1. We found the AAS values between 1,377 and 0. We found that there was no correlation between TCN and AAS as Celik et al. (31) did not find. The wide spectrum of AAS is because articles on the T100 list do not get the same level of attention in social media.

Our findings show that epidemiological studies investigating the prevalence, clinical features, and outcomes of perinatal infections have attracted great interest both in the academic community and in social media. The AAS increases in response to both positive and negative comments, this should also be considered in the assessment. Even if an article receives few citations, it might gain a lot of attention on social media. The article by Allotey et al. (26) had AAS of 1,307, but it was only 61 times cited. This can be attributed to the subject of the article attracting social media attention. Only the four of the articles with the highest AAS were among the top 10 articles with the highest TCN.

Altmetric attention scores and the number of tweets had a weak positive correlation with average citation per year (ACpY). This means that articles which have been cited regularly over the years and remain relevant are more valuable on social media and Twitter.  Furthermore, despite analyzing only the last decade, we found a strong negative correlation between the “AAS” of the T100 articles and the number of years since publication. The rising number of social media users globally, as well as social media’s growing interest in studies about the perinatal infections literature in the last few years, might explain these findings.

The “level of evidence” indicates how likely it is that a research paper’s conclusions are correct. It is related to the study’s design and how well it is carried out. The highest AAS and total citation number values were found in articles with the level of evidence 1 in our study, as expected. The h-index is a scientometric indicator at the researcher level that is based on a simple combination of publication and citation counts. In this article, we found a statistically significant and strong correlation between the journal IF and the h index.  One thing to remember about the h-index is that it correlates with the length of a researcher’s career and it can also be inflated by self-citation.

In our study, we determined a statistically significant weak correlation between the journal IF and total citation number. Also, we found that there was no correlation between IF and AAS or the number of tweets. The fact that AAS and number of tweets were positively correlated with ACpY might explain this situation. The IF would only measure the interests of other researchers in an article, not its value and usefulness. Previous studies have discovered that journals with social media accounts such as Twitter has significantly higher AAS than those without (32) and that tweets can predict highly cited articles within the first 3 days of article publication. When we investigated the relationship between AAS and average citation per year, we discovered that articles that drew attention in the academic community retained their relevance in social media.      

Almost half of the included articles were from the USA and China which was consistent with previous studies (33,34). This may be closely related to the influence and scientific output of the field of perinatal infection in the USA and China. The developed countries such as the USA pay more attention to the topic and have more funding. If we consider China, it is the country where the COVID pandemic started and spread to the world.

Study Limitations

Current definitions of altmetrics are shaped and limited by active platforms, technical capabilities, and the Altmetric.com website. Given Altmetric does not include all media sources and the relationship between AAS and citations may change over time. First, the current study was only based on journal studies from the WoS database. Therefore, there might be some overlooked literature. When we analyzed the origin country, our study was based on the institution address of the corresponding author if the author changed the address, there might be statistical bias. Altmetrics do not cover the demographics of scientists and the nature of each mention (positive or negative) Furthermore, the reliability of commenters and the veracity of their opinions are under doubt due to the ease with which internet data may be manipulated.


There is no study examining the 100 most cited articles on perinatal infections. This study reflects the most influential publications to identify the trends of current studies and provides some directions for future studies to help researchers. Also, it presents a view on the subject of the level of interest shown by the scientific world on social media platforms to the most cited articles on the subject of perinatal infections.


Ethics Committee Approval: Each author certified that the study was conducted following the ethical principles of the Helsinki Declaration. This study did not require ethical approval as it performed bibliometric and altmetric analysis of currently published articles on perinatal infections.

Peer-review: Externally peer reviewed.

Authorship Contributions

Concept: D.B., H.B.,  Design: D.B., H.B., Data Collection or Processing: D.B., H.B., Analysis or Interpretation: D.B., H.B.,  Literature Search: D.B., H.B., Writing: D.B., H.B.

Conflict of Interest: No conflict of interest was declared by the authors.

Financial Disclosure: The authors declared that this study received no financial support.


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