From to , Professor Chomsky was cited 7, times in the Social Science Citation Index-likely the greatest number of times for a living person there as well, although the research into those numbers isn't complete. In addition, from to he was cited 1, times in the Science Citation Index.
Tobin, the Humanities Librarian who checked the numbers. Massachusetts Institute of Technology. Search MIT. Search websites, locations, and people. Enter keywords to search for news articles: Submit. Nielsen and Andersen also found a decrease in the share of citations given to papers authored in the United States, and an increase in those given to papers authored in Western Europe and Australasia. He points out that the proportion of papers from US researchers has been dropping, as well.
PhD students and postdocs, for instance, might publish only one or two papers before leaving academia for jobs in industry, government or the non-profit sector. If the number of such authors is increasing, this could account for some of the apparent increase in citation concentration among senior scientists.
Andersen says that he and Nielsen had to limit their analysis to authors with multiple publications for data-quality reasons. Requiring each profile to contain multiple publications in the same field helped their algorithm to distinguish between authors with similar names; including everyone who had a single publication would have made the analysis too difficult.
Sugimoto is not too concerned about citation inequality between established authors. Ludo Waltman, a quantitative scientist at Leiden University in the Netherlands, says that the study does not show that citation inequality leads to inequality in funding or career advancement.
And he adds that some of the citation imbalance might result from elite researchers collaborating frequently with other groups and so ending up with their names on many papers. But the presence of an elite could be a problem if it influences the direction of research in a field. Nielsen points to a paper 4 that found that after a scientific luminary dies, new authors and new ideas begin to enter the field more easily.
Nielsen and Andersen are now looking at whether members of scientific elites preferentially cite each other, and whether transient scientists who work as students or technicians help to build the reputation of the elite authors who employ them. Nielsen, M. Natl Acad. USA , e PubMed Article Google Scholar. Article Google Scholar. National Science Board. Publications Output: U. The top ten keyword clusters are presented in Figure 4.
The representative terms with the highest betweenness centrality are shown for each cluster. The numbers of citable and cited articles across the keyword clusters are shown in Tables 2 and 3. Five bibliometric indices are present at the right-hand side. We found that the AIF had a weak relation with the other four indices, as shown in the bottom right side in Table 2. However, the journal impact factor is 4.
The two keyword clusters of mHealth and telemedicine earned the highest indices in comparison to their counterparts Figure 5 , indicating both topics have a higher metric ie, the normalized mean of h, g, x, and Ag than the other topic clusters.
Other authors also gained excellent citation indices on Figure 2 , such as Stoyan R Stoyanov from the United States 4 papers since , John Torous from Germany 5 papers since , Paul Krebs from Germany 3 papers since , and Kathryn Mercer from Germany 3 papers since 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.
Another new finding is about the two keyword clusters of mHealth and telemedicine with the highest metrics among types of article feature, which is rarely seen when combining citation analysis and SNA in previous articles.
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 [ 4 - 8 ] merely present the highly frequency counts of authors instead of the association of authors in a network, such as the most productive authors Urs-Vito Albrecht and Sherif M. Badawy in Figure 2 , instead of the most pivotal author Ralph Maddison with the highest BC, who is associated with many coauthors in the network.
Many data scientists have developed ways to discover new knowledge from the vast quantities of increasingly available information [ 45 ], especially by applying SNA [ 4 - 6 ] to large data analysis. 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 Figure 6 , and then checking the author without duplicate names in the network by clicking the associated coauthors in the opposite neighbor subnetworks to examine whether the author had the same names in each paper.
The dashboard [ 46 ] could easily be linked to the published papers in Medline if the author was clicked. For further details about the steps made to ensure there were no authors without duplicate names, see Multimedia Appendices 1 and 2. Furthermore, we found papers in Medline because of the keyword social network analysis Title as of May 20, However, no such study like ours has incorporated the SNA analysis with Google Maps to interpret the results.
Many papers investigated most-cited articles or most productive authors in academics. Few inspected most-cited authors in a given journal.
Furthermore, we illustrated a way to examine article topics associated with the number of citations for a journal. Previous studies [ 49 - 51 ] reported: 1 a higher impact factor being associated with the publication of reviews and original articles instead of case reports; 2 rigorous systematic reviews receiving more citations than other narrative reviews; and 3 case reports with low impact factors due to them being rarely cited by articles.
In comparison, we applied the author-defined keywords to cluster article features, which is different from previous studies in that an objective verification was made for a given journal. As such, the bibliometric metrics can be linked to the article features if each article has been assigned to its corresponding type. Regarding the incorporation of Google Maps with SNA, Google Maps are sophisticatedly linked in references [ 41 - 52 ] for readers interested in manipulating the link as a dashboard.
We hope subsequent studies can report other types of information using the Google application programming interface to readers in the future. 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 [ 53 ] and Gephi [ 54 ], are suggested to readers for use in the future. Fifth, we downloaded citing articles from PMC, which are different from many citation analyses that use other academic databases, such as the Scientific Citation Index, Scopus, and Google Scholar [ 55 - 58 ], to investigate the most cited articles in a specific discipline. This approach using data from PMC can lead to more citation studies reporting the most cited authors in other disciplines.
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.
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. PDF:using between centrality to detect authors with duplicate names in a network. Edited by G Eysenbach; submitted Skip to Main Content Skip to Footer. Article Authors Cited by 11 Tweetations 2 Metrics.
Original Paper. Table 1.
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