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Many previous papers have investigated most-cited articles or most productive authors in academics, but few have studied most-cited authors. Two challenges are faced in doing so, one of which is that some different authors will have the same name in the bibliometric data, and the second is that coauthors’ contributions are different in the article byline. No study has dealt with the matter of duplicate names in bibliometric data. Although betweenness centrality (BC) is one of the most popular degrees of density in social network analysis (SNA), few have applied the BC algorithm to interpret a network’s characteristics. A quantitative scheme must be used for calculating weighted author credits and then applying the metrics in comparison.
This study aimed to apply the BC algorithm to examine possible identical names in a network and report the most-cited authors for a journal related to international mobile health (mHealth) research.
We obtained 676 abstracts from Medline based on the keywords “JMIR mHealth and uHealth” (Journal) on June 30, 2018. The author names, countries/areas, and author-defined keywords were recorded. The BCs were then calculated for the following: (1) the most-cited authors displayed on Google Maps; (2) the geographical distribution of countries/areas for the first author; and (3) the keywords dispersed by BC and related to article topics in comparison on citation indices. Pajek software was used to yield the BC for each entity (or node). Bibliometric indices, including h-, g-, and x-indexes, the mean of core articles on g(Ag)=sum (citations on g-core/publications on g-core), and author impact factor (AIF), were applied.
We found that the most-cited author was Sherif M Badawy (from the United States), who had published six articles on JMIR mHealth and uHealth with high bibliometric indices (h=3; AIF=8.47; x=4.68; Ag=5.26). We also found that the two countries with the highest BC were the United States and the United Kingdom and that the two keyword clusters of mHealth and telemedicine earned the highest indices in comparison to other counterparts. All visual representations were successfully displayed on Google Maps.
The most cited authors were selected using the authorship-weighted scheme (AWS), and the keywords of mHealth and telemedicine were more highly cited than other counterparts. The results on Google Maps are novel and unique as knowledge concept maps for understanding the feature of a journal. The research approaches used in this study (ie, BC and AWS) can be applied to other bibliometric analyses in the future.
As of April 12, 2018, more than 146 papers were found by the keyword “author collaboration” (Title), 1168 by “author collaboration,” and 53 by “author collaboration” and “bibliometric” in the Medline Library. A phenomenal increase has been found in the number of research papers with multiple authors [
An author’s publication features can be determined by social network analysis (SNA) [
[T]here might be some biases of understanding for author collaboration because some different authors with the same name or abbreviation exist, who are affiliated to different institutions. The result of author relationship analysis for mHealth research would be influenced by the accuracy of the indexing author.
Three main centrality measures (ie, degree, closeness, and betweenness) are frequently used to evaluate the influence (or power) momentum of an entity (or the author of a study) in a network [
As of June 31, 2020, over 269 articles were found by searching the keyword “most cited” (Title) in PubMed Central (PMC) and 39 papers by “most productive author” or “most prolific author.” However, few had studied most-cited authors. The reason might be that there is no quantitative scheme that has been successfully used to calculate weighted author credits in the literature; even many counting schemes have been proposed for quantifying coauthor contributions [
The author’s publication patterns are always presented with static .jpg format pictures [
The journal of JMIR mHealth and uHealth was targeted for BC algorithm application to examine possible duplicate authors with the same names in a network. Our goal is to select the most highly cited authors in author collaborations. Also, both features (ie, the affiliation regions distributed for the first author in geography, and the keywords related to article topics) will be investigated using the citation analysis in this study.
When searching the PubMed database (Pubmed.org) maintained by the US National Library of Medicine, we used the keywords “JMIR mHealth and uHealth” (Journal) on June 30, 2018. We then downloaded 676 articles that had been published since 2013, because the first article in JMIR mHealth and uHealth was published in 2013. An author-made Microsoft Excel (Microsoft Corporation, Albuquerque, New Mexico, United States) VBA (visual basic for applications) module was used to analyze the research data. All downloaded abstracts were based on the type of journal article involved. Ethical approval was not necessary for this study because all the data were obtained online from the Medline library.
SNA [
Centrality is a vital index for analyzing a network. Any individual or keyword in the center of a social network will determine its influence on the network and its speed at gaining information [
By contrast, the BC of node v, which is denoted as g(v), is obtained as svt in Standalone Equation 1. The BC of node v is the number of shortest paths from node s to node t (s,t≠v). Finally, the BC should be divided by the possible number of connected nodes, (N-1)(N-2)/2, where N is the number of nodes in the network. If all the nodes go through v in the shortest path, g(v) is equal to 1.
The BC for node b is calculated in
Calculation of betweenness centrality.
The two nodes (ie, a and e) have two equal shortest paths (ie, abce and abde). The number of shortest paths from node a to node e is 2.
The method used to ensure there are no authors with duplicate names in the network is to identify the large bubble (with high BC) by clicking the linked coauthors and checking if the author is identical between any two neighbor subnetworks (see
The AWS and the author impact factor (AIF) calculations are shown in Standalone Equations 3 and 4:
Considering a paper of m+1 authors with the last being the corresponding author, Wj denotes the weight for an author on the order j in the article byline. The power, γj, is an integer number from m–1 to 0 in descending order. The sum of author weights in a byline is Standalone Equation 5.
The sum of authorships equals 1 for each paper referred to in Standalone Equation 5. This is a basic concept ensuring that all papers have an equal weight irrespective of the number of coauthors [
We selected JMIR mHealth and uHealth as the target journal. The authors (n1=3522) (see
The countries/areas of authors for each published paper were extracted to show the distribution of countries/areas on Google Maps using choropleth maps [
The most-cited author is Sherif M Badawy (from the United States), who published six articles on JMIR mHealth and uHealth with high bibliometric indices (h=3; AIF=8.47; x=4.68; Ag=5.26). His top five weighted citations are 9.5 ,7.6, 7.3, 1.3, and 0.5, which yield an h-index of 3 at the third position due to the fourth cited value (1.3) being less than the paper number of 4. The Ag (5.26) and x-index (4.68) are yielded because of g being at 5 (ie, the total citations (26.29) are greater than 25) and x at 3 [ci = 7.3 when computing
Authors’ citations dispersed on Google Maps.
The top six countries with the highest increase in number of production outputs (ie, Growth>0.90) were the United States, the United Kingdom, South Korea, Canada, Australia, and New Zealand (
Dispersion of country/area on author collaborations for JMIR mHealth and uHealth.
Dispersions of author collaboration across continents over the years
Continent, Country | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | Total, n (%) | Growtha | x-index | |
|
—b | 2 | 1 | 2 | 2 | 1 | 8 (1.18) | 0.71 | — | |
|
Kenya | — | — | 1 | — | — | — | 1 (0.15) | — | 1.95 |
|
Nigeria | — | — | — | — | 1 | — | 1 (0.15) | 0.71 | — |
|
South Africa | — | 2 | — | 2 | 1 | — | 5 (0.74) | 0.32 | 2.42 |
|
Uganda | — | — | — | — | — | 1 | 1 (0.15) | — | — |
|
3 | 10 | 8 | 9 | 22 | 32 | 84 (12.43) | 0.83 | — | |
|
China | 2 | 2 | 1 | 1 | 7 | 12 | 25 (3.7) | 0.57 | 3.19 |
|
South Korea | — | — | 2 | 2 | 4 | 6 | 14 (2.07) | 0.94 | 3.08 |
|
Singapore | — | 3 | — | — | 1 | 4 | 8 (1.18) | –0.12 | 3.56 |
|
Thailand | — | 2 | 2 | — | 1 | 2 | 7 (1.04) | — | 2.25 |
|
Taiwan | — | — | — | 1 | 2 | 3 | 6 (0.89) | 0.88 | 1.39 |
|
Others | 1 | 3 | 3 | 5 | 7 | 5 | 24 (3.55) | 0.97 | — |
|
15 | 12 | 18 | 35 | 60 | 67 | 207 (30.62) | 0.89 | — | |
|
United Kingdom | 2 | — | 9 | 9 | 13 | 12 | 45 (6.66) | 0.91 | 6.65 |
|
Germany | 2 | 2 | 1 | 2 | 11 | 11 | 29 (4.29) | 0.68 | 5.97 |
|
Spain | 5 | 1 | 1 | 4 | 5 | 10 | 26 (3.85) | 0.23 | 5.41 |
|
Netherlands | 1 | — | 1 | 9 | 7 | 6 | 24 (3.55) | 0.81 | 4.7 |
|
Sweden | — | 3 | 4 | 4 | 3 | 4 | 18 (2.66) | 0.67 | 4.84 |
|
Others | 5 | 6 | 2 | 7 | 21 | 24 | 65 (9.62) | 0.71 | — |
|
6 | 21 | 52 | 70 | 90 | 54 | 293 (43.34) | 0.99 | — | |
|
United States | 6 | 17 | 42 | 58 | 79 | 47 | 249 (36.83) | 0.99 | 17.13 |
|
Canada | — | 4 | 10 | 12 | 11 | 7 | 44 (6.51) | 0.92 | 8.74 |
|
1 | 9 | 15 | 21 | 19 | 11 | 76 (11.24) | 0.93 | — | |
|
Australia | 1 | 8 | 13 | 17 | 15 | 10 | 64 (9.47) | 0.91 | 11.03 |
|
New Zealand | — | 1 | 2 | 4 | 4 | 1 | 12 (1.78) | 0.97 | 4.81 |
|
— | 3 | 1 | — | 3 | 1 | 8 (1.18) | 0.31 | — | |
|
Brazil | — | 2 | — | — | 2 | 1 | 5 (0.74) | 0.29 | 2.52 |
|
Colombia | — | 1 | — | — | — | — | 1 (0.15) | –0.35 | 1.59 |
|
Peru | — | — | 1 | — | 1 | — | 2 (0.3) | 0.58 | 1.59 |
Total | 25 | 57 | 95 | 137 | 196 | 166 | 676 (100) | 0.99 | 26.56 |
aGrowth based on data from 2013 and 2017.
bNot applicable.
The top ten keyword clusters are presented in
Dispersion of keyword clusters for the first author clusters of JMIR mHealth and uHealth. mHealth: mobile health.
The numbers of citable and cited articles across the keyword clusters are shown in
Bibliometric indices for medical subject heading (MeSH) terms over the years for publications.
Keywords | Publication count | AIFa | h | g | x | (g)Agb | ||||||
|
2013 (n) | 2014 (n) | 2015 (n) | 2016 (n) | 2017 (n) | 2018 (n) | Total (N) |
|
||||
Text messaging | —c | 4 | 4 | 5 | 6 | 6 | 25 | 4 | 7 | 9 | 7.48 | 9.67 |
mHealthd | 7 | 16 | 39 | 51 | 68 | 55 | 236 | 4.4 | 16 | 21 | 19.13 | 21.57 |
Physical activity | 2 | 3 | 4 | 8 | 16 | 14 | 47 | 2.83 | 6 | 11 | 7.21 | 11.18 |
Telemedicine | 2 | 11 | 18 | 33 | 57 | 51 | 172 | 4.87 | 15 | 23 | 16.43 | 24.26 |
Mobile health | 3 | 8 | 9 | 14 | 21 | 15 | 70 | 4.6 | 10 | 13 | 12.41 | 14.08 |
Ecological momentary |
— | — | 1 | 2 | 2 | 1 | 6 | 1.17 | 1 | 1 | 2.24 | 5 |
Internet | 3 | 4 | 6 | 3 | 5 | 4 | 25 | 7.36 | 8 | 13 | 9.54 | 14 |
Obesity | 1 | 2 | 5 | 8 | 4 | 1 | 21 | 5.9 | 6 | 10 | 6.93 | 10.4 |
Wearable | — | — | 1 | — | 1 | 3 | 5 | 1 | 1 | 1 | 2 | 3 |
Mobile phone | 1 | 2 | 2 | 6 | 3 | 2 | 16 | 3.56 | 5 | 7 | 5.48 | 7.29 |
Others | 6 | 7 | 6 | 6 | 13 | 10 | 48 | 2.63 | — | — | — | — |
Total | 25 | 57 | 95 | 136 | 196 | 162 | 671 | 4.37 | — | — | — | — |
aAIF: author impact factor.
b(g)Ag: publications on g-core.
cNot applicable.
dmHealth: mobile health.
Correlation coefficients of metrics for medical subject heading (MeSH) terms over the years for quantity of citations.
Keywords | Publication count | Correlation | AIFa | h | g | x | (g)Agb | ||||||
|
2013 (n) | 2014 (n) | 2015 (n) | 2016 (n) | 2017 (n) | 2018 (n) | Total (N) |
|
|||||
Text messaging | —c | 28 | 28 | 30 | 14 | 0 | 100 | AIF | 1 | — | — | — | — |
mHealthd | 112 | 212 | 335 | 242 | 131 | 7 | 1039 | h | 0.57 | 1 | — | — | — |
Physical activity | 25 | 18 | 19 | 48 | 23 | 0 | 133 | g | 0.63 | 0.98 | 1 | — | — |
Telemedicine | 46 | 182 | 307 | 186 | 95 | 22 | 838 | x | 0.54 | 0.99 | 0.96 | 1 | — |
Mobile health | 11 | 82 | 91 | 100 | 38 | 0 | 322 | Ag | 0.58 | 0.98 | 0.99 | 0.96 | 1 |
Ecological momentary assessment | — | — | 2 | 5 | 0 | 0 | 7 | — | — | — | — | — | — |
Internet | 33 | 57 | 81 | 9 | 4 | 0 | 184 | — | — | — | — | — | — |
Obesity | 16 | 12 | 59 | 25 | 12 | 0 | 124 | — | — | — | — | — | — |
Wearable | — | — | 3 | — | 2 | 0 | 5 | — | — | — | — | — | — |
Mobile phone | 7 | 10 | 25 | 15 | 0 | 0 | 57 | — | — | — | — | — | — |
Others | 20 | 35 | 46 | 23 | 2 | 0 | 126 | — | — | — | — | — | — |
Total | 270 | 636 | 996 | 683 | 321 | 29 | 2935 | — | — | — | — | — | — |
aAIF: author impact factor.
b(g)Ag: publications on g-core.
cNot applicable.
dmHealth: mobile health.
Comparison of article topics related to bibliometric indices. Ag: publication on g-core.
We found that the most-cited author is Sherif M Badawy (from the United States), who has published six articles on JMIR mHealth since 2016. Other authors also gained excellent citation indices on
The most productive authors with six papers were Urs-Vito Albrecht (citable=2.6; cited=18.1; AIF=6.8) from Germany, and Sherif M. Badawy (citable=3.3; cited=27.7; AIF=8.5) from the United States. The reason why Badawy has a higher weighted value of citable papers than Albrecht is that the latter was the middle author more often than the former if the AWS in Standalone Equation 3 was applied. If the BCs were applied, the author Ralph Maddison, from Australia, who had five papers (citable=1.1; cited=6.1; AIF=5.5), played the most pivotal (bridge) role in the authoring network.
The two countries with the highest BC were the United States (x-index=17.13) and the United Kingdom (x-index=6.65), thereby proving that the United States and Europe still dominate publication output in science [
Traditionally, in dealing with a test with multiple questions and answers, we often count the item with the highest frequency as representing the most important value. For instance, many customers purchase their goods in a shopping cart, which is like a test of multiple answers without considering any associations between entities. Accordingly, many articles [
We also ensured that no author had duplicate names in the network via identification of the large bubble (ie, with a high BC) first by clicking the linked coauthors (eg, Francois Modave at the left-bottom bubble in
Author clusters in a collaboration network.
Furthermore, we found 335 papers in Medline because of the keyword social network analysis (Title) as of May 20, 2018. In practice, we found studies on duplicative prescriptions using SNA in Japan [
Previous studies [
Regarding the incorporation of Google Maps with SNA, Google Maps are sophisticatedly linked in references [
Although findings were based on the above analysis, the results should be interpreted with caution because of several potential limitations. First, this study only focused on a single journal. Any generalization should be made in similar fields of journal contents. Second, although SNA is quite useful in exploring the topic evolution and identifying hotspots for keywords, the results might be affected by the accuracy of the author-defined terms. The medical subject heading (MeSH) terms included in the PubMed library are recommended for use in the future. Third, many different algorithms are used for SNA. We merely applied community cluster and density with BC in the figures. Any changes made along with the algorithm will present different patterns and inferences. Fourth, SNA is not subject to the Pajek software we used in this study. Others, such as Ucinet [
The most cited authors were selected using the authorship-weighted scheme (AWS). The keywords of mHealth and telemedicine are potentially highly cited more than other types of keywords. The results on Google Maps are novel and unique as a knowledge concept maps for understanding the features of a journal. The research approaches used in this study (ie, BC and AWS) can be applied to other bibliometric analyses in the future.
MP4: Identifying the unique author name.
PDF:using between centrality to detect authors with duplicate names in a network.
Txt:Pajek control file and dataset.
MP4”How to deal with data and build the Google maps.
MP4: MS Excel module extracting data from a website and plotting Google Maps.
author impact factor
authorship-weighted scheme
betweenness centrality
citations on g-core/publications on g-core
medical subject heading
PubMed Central
social network analysis
visual basic for applications
WC conceived and designed the study. WC and TW performed the statistical analyses and were in charge of dealing with data. YT and WC helped design the study, collected information, and interpreted data. PH monitored the research. All authors read and approved the final article.
None declared.