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Citation counts for peerreviewed articles and the impact factor of journals have long been indicators of article importance or quality. In the Web 2.0 era, growing numbers of scholars are using scholarly social network tools to communicate scientific ideas with colleagues, thereby making traditional indicators less sufficient, immediate, and comprehensive. In these new situations, the altmetric indicators offer alternative measures that reflect the multidimensional nature of scholarly impact in an immediate, open, and individualized way. In this direction of research, some studies have demonstrated the correlation between altmetrics and traditional metrics with different samples. However, up to now, there has been relatively little research done on the dimension and interaction structure of altmetrics.
Our goal was to reveal the number of dimensions that altmetric indicators should be divided into and the structure in which altmetric indicators interact with each other.
Because an articlelevel metrics dataset is collected from scholarly social media and open access platforms, it is one of the most robust samples available to study altmetric indicators. Therefore, we downloaded a large dataset containing activity data in 20 types of metrics present in 33,128 academic articles from the application programming interface website. First, we analyzed the correlation among altmetric indicators using Spearman rank correlation. Second, we visualized the multiple correlation coefficient matrixes with graduated colors. Third, inputting the correlation matrix, we drew an MDS diagram to demonstrate the dimension for altmetric indicators. For correlation structure, we used a social network map to represent the social relationships and the strength of relations.
We found that the distribution of altmetric indicators is significantly nonnormal and positively skewed. The distribution of downloads and page views follows the Pareto law. Moreover, we found that the Spearman coefficients from 91.58% of the pairs of variables indicate statistical significance at the .01 level. The nonmetric MDS map divided the 20 altmetric indicators into three clusters: traditional metrics, active altmetrics, and inactive altmetrics. The social network diagram showed two subgroups that are tied to each other but not to other groups, thus indicating an intersection between altmetrics and traditional metric indicators.
Altmetrics complement, and most correlate significantly with, traditional measures. Therefore, in future evaluations of the social impact of articles, we should consider not only traditional metrics but also active altmetrics. There may also be a transfer phenomenon for the social impact of academic articles. The impact transfer path has transfer, or intermediate, stations that transport and accelerate article social impact from active altmetrics to traditional metrics and vice versa. This discovery will be helpful to explain the impact transfer mechanism of articles in the Web 2.0 era. Hence, altmetrics are in fact superior to traditional filters for assessing scholarly impact in multiple dimensions and in terms of social structure.
The evaluation of an academic paper’s influence is important for scientists and academic management mechanisms [
Impact factor is based on journals, not journal articles. It is unlikely that one type of metric (for example, citation counts) can adequately inform evaluations across multiple disciplines, departments, career stages, and job types. In addition, a newly published article requires time to accumulate citations—a citation delay may range from 3 months to 12 years, sometimes longer in formal publications. By contrast, only a few days are required to tabulate statistics from viewing, downloading, tags, digs, tweets, and blogs in scientific social networks.
A reasonable evaluation should include not only quantitative assessments but also the peerreview process. The traditional peerreview process has been criticized for its scalability, that is, the inability to cope with an increasingly large number of scientific paper submissions, given the limited number of available reviewers and publication time constraints.
With the development of the open access platform [
Hence, researchers and publishers are exploring articlelevel metrics, which include not only citation rates but also potential extracted indicators such as page view, download, click, note, recommend, tag, post, trackback, and comments [
In light of the advantage of altmetrics, many authors have called for its further evaluation. Neylon and Wu [
Do altmetrics correlate with traditional measures? Some researchers have studied this question and provided evidence that altmetric and traditional indicators correlate significantly. For example, Yan and Gerstein [
In summary, all previous researchers have focused on demonstrating the performance of altmetric indicators and correlations between traditional and altmetric indicators. However, important questions such as the dimensionality and structure of altmetrics have not been explored. In other words, the overall configuration is unclear and requires further verification. For example, how many dimensions should altmetric indicators be divided into? How does the interactive structure look? Motivated by these questions, we attempt to look into the similarities and the differences between traditional and altmetric indicators. We will represent the interactive structure visually in a social network context.
For our study, it is vital to make sure altmetric indicators have the attributes of openness and maneuverability for samples before conducting an altmetrics study. The publisher platforms where articles are being written, read, and published, such as JMIR, PLOS, and social networks, such as Twitter, CiteULike, blogs, or Mendeley, where articles are being shared, recommended, discussed, and rated, make their data available through standardized application programming interfaces (APIs), which allow authors, editors, and academic administration to select the most meaningful data for a particular use at a particular time. These individuals could thus showcase a wider range of article impact in an immediate, open, and individualized way.
ArticleLevel Metrics represent a comprehensive set of impact indicators that capture usage, citations, social bookmarking and dissemination activity, media and blog coverage, discussion activity, and ratings. API for ArticleLevel Metrics is freely and publicly available. More than 150 developers have downloaded the API for data reuse to determine the total impact of articles. Hence, we consider these data to provide a good sample for our study.
On selection of the tests, we considered the options proposed by researchers, such as graduated colors for correlation coefficient matrixes [
We downloaded an “ArticleLevel Metrics” dataset (specifically, a sample of 33,128 academic articles) from the PLOS API website on December 14, 2011. The dataset includes data for a number of metrics, for example, counts of article usage, citation rates, and other types of metrics (eg, social bookmarks, comments, notes, blog posts, and ratings). We noticed that the values of the altmetric variables differ too markedly in dimensions, thus resulting in smaller absolute values weighing less when calculating the distances between values. Therefore, variables were handled as dimensionless with an algorithm “mean of 1” to keep the coefficients of the original variables constant [
First, we drew a histogram to discern approximately whether the data followed a normal distribution. In a normal distribution, the 2 “halves” of the histogram appear as mirror images of each other [
Second, a correlation, indicated by a correlation coefficient, measures the strength and the direction of a linear relationship between two variables [
Third, MDS could generate a visual representation of the subjective dimensions that are not directly indicated in the data [
Fourth, an MDS diagram can reveal the similarities among variables, though not the strength and the structure of the relationships among variables. Visualizing the correlation matrix in a network context is useful. Researchers observe social relationships based on the theory that a social network comprises nodes and ties. Nodes represent individual actors within the network, and ties represent relationships between variables and individuals [
We used a onesample KS test to determine whether the altmetric variables are normally distributed. In general, if
We also drew histograms and obtained a group of skewed histograms. Because variable
As shown in the histogram of downloads and page views (from D to G), the data have two relative peaks that follow a bimodal distribution, similar in appearance to the back of a twohumped camel. This distribution is reminiscent of the Pareto Principle (or the 8020 rule), that is, approximately 80% of the effects arise from 20% of the causes [
Integration of the results including KS, skewness, and kurtosis of variables (N=33,128).

KS  Skewness  Kurtosis  
Z  Asymp sig^{a} (2tailed)  S  SE^{b}  K  SE  
B1  59.205  <.001  7.271  0.013  88.479  0.027 
B2  73.141  <.001  15.342  0.013  299.692  0.027 
B3  64.031  <.001  10.778  0.013  218.256  0.027 
B4  79.442  <.001  27.596  0.013  1405.772  0.027 
B5  83.492  <.001  7.902  0.013  128.074  0.027 
B6  70.240  <.001  15.694  0.013  405.590  0.027 
B7  81.168  <.001  21.363  0.013  908.977  0.027 
B8  94.749  <.001  18.727  0.013  421.814  0.027 
B9  92.407  <.001  60.796  0.013  4051.419  0.027 
B10  94.019  <.001  17.715  0.013  436.739  0.027 
B11  88.133  <.001  29.028  0.013  1075.095  0.027 
B12  72.099  <.001  20.820  0.013  916.229  0.027 
B13  93.919  <.001  16.017  0.013  359.758  0.027 
B14  91.698  <.001  25.092  0.013  1010.583  0.027 
B15  93.719  <.001  19.392  0.013  770.375  0.027 
B16  91.612  <.001  17.786  0.013  566.946  0.027 
B17  93.434  <.001  40.112  0.013  2102.985  0.027 
B18  87.564  <.001  17.025  0.013  556.158  0.027 
B19  92.924  <.001  32.325  0.013  1447.864  0.027 
B20  94.296  <.001  27.080  0.013  1205.024  0.027 
^{a}asymptotic significance
^{b}standard error
Legends for B1 to B20.
Altmetric indicators  Legends 
B1  Citations recorded by CrossRef 
B2  Citations recorded by PubMed Central 
B3  Citations recorded by Scopus 
B4  Total HTML page views 
B5  Total PDF downloads 
B6  Total XML downloads 
B7  Combined usage (HTML + PDF + XML) 
B8  Blog postings indexed by Nature Blogs 
B9  Blog postings indexed by Bloglines 
B10  Blog postings indexed by ResearchBlogging.org 
B11  Trackbacks made by external sites 
B12  Social bookmarking made by users of CiteULike 
B13  Social bookmarking made by users of Connotea 
B14  Ratings on PLOS website 
B15  Average rating that the article has received 
B16  Note threads started on the article 
B17  Replies to Note thread 
B18  Comment threads started on the article 
B19  Replies to Comment threads 
B20  “Star Ratings” including a text comment 
Histograms of frequency distribution for altmetric indicators.
We performed the normality test to conclude that neither altmetric variable is normally distributed. We used a Spearman rank order correlation to examine the correlation pattern among altmetric indicators with SPSS 18.0.
The correlation coefficient can range from 1 to 1, with 1 or 1 indicating a perfect relationship [
The second strongest correlation, rho=.899, is between total HTML page views (expressed by B4) and total PDF downloads (expressed by B5), possibly because they are two aspects of article usage counts, and people choose view or download with approximately equivalent frequencies. The Spearman rho between B6 and B13 is –.25, so we can predict that as B6 (total XML downloads) increases, B13 (social bookmarking made by Connotea users) will decrease.
The Spearman coefficients from 91.58% of the variable pairs are significant at the .01 level, with one pair of variables (B9 and B12) correlating at the .05 significance level. Approximately no correlation exists between approximately 7.89% variable pairs. B9 (blog postings indexed by the Bloglines) also hardly correlate with eight variables (ie, B13 to B20), possibly implying that Blogline is unpopular and not widely used by researchers and citizen scientists.
The correlation matrix also yields the probability of being incorrect if we assume that the relationship observed in our sample accurately reflects the relationship among variables of altmetric indicators in the actual population from which the sample was drawn, labeled as Sig (2tailed). We found that 91.58% of the probability value is <.001 (the value is rounded to three digits), well below the conventional threshold of
To show the correlation among altmetrics clearly, we visualized the correlation coefficient matrices with graduated colors and a bluewhitered scale. An R programming package, corrplot, helped map the correlation coefficients to the specified color square. We chose two color series to identify positive and negative correlation coefficients. Blue corresponds to a correlation of approximately 1; red to approximately –1; and white to approximately 0. To economize space, we multiplied the correlation coefficients by 100 and added them to the squares in the color correlation matrix. See
We can readily identify clusters with strong similarities and locate possible redundant indicators. Matching this map with the physical meaning revealed the following: (1) the citation indicators (B1, B2, and B3) and download indicators (B4, B5, B6, and B7) are clustered into two categories, which we call the “citation metrics class” and “download metrics class”, respectively; (2) the citation and download indicators are combined into a clustering, which we call the “traditional metrics class”; (3) a group of indicators (B14 to B20) are conjoined into another clustering type, called the “rating, note, and comment metrics class”; and (4) finally, as a general rule, we suggested that all four blogaggregating services would record different sets of data, so the datasets require comparison and “deduplication” to obtain a complete picture of blog activity (as recorded by these services), as would all three citation services.
Spearman rank correlation coefficient for B1B11 (N=33,128).

B1  B2  B3  B4  B5  B6  B7  B8  B9  B10  
B1  Corr. coefficient  1.000  .599^{a}  .738^{a}  .322^{a}  .378^{a}  .153^{a}  .338^{a}  .050^{a}  .019^{a}  .088^{a} 
Sig (2tailed) 

<.001  <.001  <.001  <.001  <.001  <.001  <.001  <.001  <.001  
B2  Corr. coefficient  .599^{a}  1.000  .669^{a}  .226^{a}  .268^{a}  .079^{a}  .237^{a}  .051^{a}  .023^{a}  .063^{a} 
Sig (2tailed)  <.001 

<.001  <.001  <.001  <.001  <.001  <.001  <.001  <.001  
B3  Corr. coefficient  .738^{a}  .669^{a}  1.000  .402^{a}  .444^{a}  .244^{a}  .415^{a}  .040^{a}  .020^{a}  .084^{a} 
Sig (2tailed)  <.001  <.001 

<.001  <.001  <.001  <.001  <.001  <.001  <.001  
B4  Corr. coefficient  .322^{a}  .226^{a}  .402^{a}  1.000  .899^{a}  .662^{a}  .996^{a}  .070^{a}  .002  .156^{a} 
Sig (2tailed)  <.001  <.001  <.001 

<.001  <.001  <.001  <.001  .784  <.001  
B5  Corr. coefficient  .378^{a}  .268^{a}  .444^{a}  .899^{a}  1.000  .616^{a}  .928^{a}  .065^{a}  .002  .125^{a} 
Sig (2tailed)  <.001  <.001  <.001  <.001 

<.001  <.001  <.001  .695  <.001  
B6  Corr. coefficient  .153^{a}  .079^{a}  .244^{a}  .662^{a}  .616^{a}  1.000  .672^{a}  .039^{a}  .005  .089^{a} 
Sig (2tailed)  <.001  <.001  <.001  <.001  <.001 

<.001  <.001  .399  <.001  
B7  Corr. coefficient  .338^{a}  .237^{a}  .415^{a}  .996^{a}  .928^{a}  .672^{a}  1.000  .070^{a}  .001  .153^{a} 
Sig (2tailed)  <.001  <.001  <.001  <.001  <.001  <.001 

<.001  .892  <.001  
B8  Corr. coefficient  .050^{a}  .051^{a}  .040^{a}  .070^{a}  .065^{a}  .039^{a}  .070^{a}  1.000  .019^{a}  .158^{a} 
Sig (2tailed)  <.001  <.001  <.001  <.001  <.001  <.001  <.001 

<.001  <.001  
B9  Corr. coefficient  .019^{a}  .023^{a}  .020^{a}  .002  .002  .005  .001  .019^{a}  1.000  .001 
Sig (2tailed)  <.001  <.001  <.001  .784  .695  .399  .892  <.001 

.918  
B10  Corr. coefficient  .088^{a}  .063^{a}  .084^{a}  .156^{a}  .125^{a}  .089^{a}  .153^{a}  .158^{a}  .001  1.000 
Sig (2tailed)  <.001  <.001  <.001  <.001  <.001  <.001  <.001  <.001  .918 


B11  Corr. coefficient  .072^{a}  .061^{a}  .073^{a}  .063^{a}  .004  .015^{a}  .053^{a}  .071^{a}  .035^{a}  .214^{a} 
Sig (2tailed)  <.001  <.001  <.001  <.001  .426  .006  <.001  <.001  <.001  <.001  
B12  Corr. coefficient  .240^{a}  .248^{a}  .222^{a}  .288^{a}  .299^{a}  .156^{a}  .293^{a}  .086^{a}  .014^{b}  .128^{a} 
Sig (2tailed)  <.001  <.001  <.001  <.001  <.001  <.001  <.001  <.001  .014  <.001  
B13  Corr. coefficient  .120^{a}  .159^{a}  .102^{a}  .065^{a}  .071^{a}  .025^{a}  .067^{a}  .042^{a}  .010  .031^{a} 
Sig (2tailed)  <.001  <.001  <.001  <.001  <.001  <.001  <.001  <.001  .071  <.001  
B14  Corr. coefficient  .074^{a}  .075^{a}  .072^{a}  .087^{a}  .057^{a}  .050^{a}  .085^{a}  .055^{a}  .005  .101^{a} 
Sig (2tailed)  <.001  <.001  <.001  <.001  <.001  <.001  <.001  <.001  .328  <.001  
B15  Corr. coefficient  .074^{a}  .076^{a}  .072^{a}  .087^{a}  .057^{a}  .050^{a}  .085^{a}  .055^{a}  .005  .101^{a} 
Sig (2tailed)  <.001  <.001  <.001  <.001  <.001  <.001  <.001  <.001  .350  <.001  
B16  Corr. coefficient  .069^{a}  .061^{a}  .067^{a}  .075^{a}  .053^{a}  .042^{a}  .073^{a}  .028^{a}  .001  .070^{a} 
Sig (2tailed)  <.001  <.001  <.001  <.001  <.001  <.001  <.001  <.001  .794  <.001  
B17  Corr. coefficient  .027^{a}  .021^{a}  .027^{a}  .045^{a}  .024^{a}  .026^{a}  .043^{a}  .036^{a}  .002  .058^{a} 
Sig (2tailed)  <.001  <.001  <.001  <.001  <.001  <.001  <.001  <.001  .662  <.001  
B18  Corr. coefficient  .090^{a}  .101^{a}  .096^{a}  .099^{a}  .056^{a}  .043^{a}  .097^{a}  .063^{a}  .010  .133^{a} 
Sig (2tailed)  <.001  <.001  <.001  <.001  <.001  <.001  <.001  <.001  .063  <.001  
B19  Corr. coefficient  .058^{a}  .055^{a}  .057^{a}  .082^{a}  .053^{a}  .041^{a}  .079^{a}  .052^{a}  .008  .103^{a} 
Sig (2tailed)  <.001  <.001  <.001  <.001  <.001  <.001  <.001  <.001  .137  <.001  
B20  Corr. coefficient  .050^{a}  .049^{a}  .049^{a}  .062^{a}  .043^{a}  .035^{a}  .060^{a}  .037^{a}  .009  .073^{a} 
Sig (2tailed)  <.001  <.001  <.001  <.001  <.001  <.001  <.001  <.001  .090  <.001 
^{a}Correlation is significant at the .01 level (2tailed).
^{b}Correlation is significant at the .05 level (2tailed).
Spearman rank correlation coefficient for B12B20 (N=33,128).

B11  B12  B13  B14  B15  B16  B17  B18  B19  B20  
B1  Corr. coefficient  .072^{a}  .240^{a}  .120^{a}  .074^{a}  .074^{a}  .069^{a}  .027^{a}  .090^{a}  .058^{a}  .050^{a} 
Sig (2tailed)  <.001  <.001  <.001  <.001  <.001  <.001  <.001  <.001  <.001  <.001  
B2  Corr. coefficient  .061^{a}  .248^{a}  .159^{a}  .075^{a}  .076^{a}  .061^{a}  .021^{a}  .101^{a}  .055^{a}  .049^{a} 
Sig (2tailed)  <.001  <.001  <.001  <.001  <.001  <.001  <.001  <.001  <.001  <.001  
B3  Corr. coefficient  .073^{a}  .222^{a}  .102^{a}  .072^{a}  .072^{a}  .067^{a}  .027^{a}  .096^{a}  .057^{a}  .049^{a} 
Sig (2tailed)  <.001  <.001  <.001  <.001  <.001  <.001  <.001  <.001  <.001  <.001  
B4  Corr. coefficient  .063^{a}  .288^{a}  .065^{a}  .087^{a}  .087^{a}  .075^{a}  .045^{a}  .099^{a}  .082^{a}  .062^{a} 
Sig (2tailed)  <.001  <.001  <.001  <.001  <.001  <.001  <.001  <.001  <.001  <.001  
B5  Corr. coefficient  .004  .299^{a}  .071^{a}  .057^{a}  .057^{a}  .053^{a}  .024^{a}  .056^{a}  .053^{a}  .043^{a} 
Sig (2tailed)  .426  <.001  <.001  <.001  <.001  <.001  <.001  <.001  <.001  <.001  
B6  Corr. coefficient  .015^{a}  .156^{a}  .025^{a}  .050^{a}  .050^{a}  .042^{a}  .026^{a}  .043^{a}  .041^{a}  .035^{a} 
Sig (2tailed)  .006  <.001  <.001  <.001  <.001  <.001  <.001  <.001  <.001  <.001  
B7  Corr. coefficient  .053^{a}  .293^{a}  .067^{a}  .085^{a}  .085^{a}  .073^{a}  .043^{a}  .097^{a}  .079^{a}  .060^{a} 
Sig (2tailed)  <.001  <.001  <.001  <.001  <.001  <.001  <.001  <.001  <.001  <.001  
B8  Corr. coefficient  .071^{a}  .086^{a}  .042^{a}  .055^{a}  .055^{a}  .028^{a}  .036^{a}  .063^{a}  .052^{a}  .037^{a} 
Sig (2tailed)  <.001  <.001  <.001  <.001  <.001  <.001  <.001  <.001  <.001  <.001  
B9  Corr. coefficient  .035^{a}  .014^{b}  .010  .005  .005  .001  .002  .010  .008  .009 
Sig (2tailed)  <.001  .014  .071  .328  .350  .794  .662  .063  .137  .090  
B10  Corr. coefficient  .214^{a}  .128^{a}  .031^{a}  .101^{a}  .101^{a}  .070^{a}  .058^{a}  .133^{a}  .103^{a}  .073^{a} 
Sig (2tailed)  <.001  <.001  <.001  <.001  <.001  <.001  <.001  <.001  <.001  <.001  
B11  Corr. coefficient  1.000  .078^{a}  .059^{a}  .125^{a}  .124^{a}  .073^{a}  .068^{a}  .148^{a}  .128^{a}  .096^{a} 
Sig (2tailed) 

<.001  <.001  <.001  <.001  <.001  <.001  <.001  <.001  <.001  
B12  Corr. coefficient  .078^{a}  1.000  .194^{a}  .098^{a}  .097^{a}  .067^{a}  .045^{a}  .097^{a}  .073^{a}  .064^{a} 
Sig (2tailed)  <.001 

<.001  <.001  <.001  <.001  <.001  <.001  <.001  <.001  
B13  Corr. coefficient  .059^{a}  .194^{a}  1.000  .050^{a}  .049^{a}  .031^{a}  .007  .060^{a}  .023^{a}  .039^{a} 
Sig (2tailed)  <.001  <.001 

<.001  <.001  <.001  .215  <.001  <.001  <.001  
B14  Corr. coefficient  .125^{a}  .098^{a}  .050^{a}  1.000  1.000^{a}  .097^{a}  .109^{a}  .190^{a}  .143^{a}  .602^{a} 
Sig (2tailed)  <.001  <.001  <.001 

<.001  <.001  <.001  <.001  <.001  <.001  
B15  Corr. coefficient  .124^{a}  .097^{a}  .049^{a}  1.000^{a}  1.000  .096^{a}  .107^{a}  .189^{a}  .141^{a}  .599^{a} 
Sig (2tailed)  <.001  <.001  <.001  <.001 

<.001  <.001  <.001  <.001  <.001  
B16  Corr. coefficient  .073^{a}  .067^{a}  .031^{a}  .097^{a}  .096^{a}  1.000  .283^{a}  .116^{a}  .112^{a}  .065^{a} 
Sig (2tailed)  <.001  <.001  <.001  <.001  <.001 

<.001  <.001  <.001  <.001  
B17  Corr. coefficient  .068^{a}  .045^{a}  .007  .109^{a}  .107^{a}  .283^{a}  1.000  .085^{a}  .132^{a}  .094^{a} 
Sig (2tailed)  <.001  <.001  .215  <.001  <.001  <.001 

<.001  <.001  <.001  
B18  Corr. coefficient  .148^{a}  .097^{a}  .060^{a}  .190^{a}  .189^{a}  .116^{a}  .085^{a}  1.000  .448^{a}  .127^{a} 
Sig (2tailed)  <.001  <.001  <.001  <.001  <.001  <.001  <.001 

<.001  <.001  
B19  Corr. coefficient  .128^{a}  .073^{a}  .023^{a}  .143^{a}  .141^{a}  .112^{a}  .132^{a}  .448^{a}  1.000  .105^{a} 
Sig (2tailed)  <.001  <.001  <.001  <.001  <.001  <.001  <.001  <.001 

<.001  
B20  Corr. coefficient  .096^{a}  .064^{a}  .039^{a}  .602^{a}  .599^{a}  .065^{a}  .094^{a}  .127^{a}  .105^{a}  1.000 
Sig (2tailed)  <.001  <.001  <.001  <.001  <.001  <.001  <.001  <.001  <.001 

^{a}Correlation is significant at the .01 level (2tailed).
^{b}Correlation is significant at the .05 level (2tailed).
Visualization of the correlation matrix in R.
Nonmetric MDS is often preferred because it tends to provide a better “goodnessoffit” (stress) statistic, which is correspondingly better with lower stress (0=perfect fit) [
The reliability value stress was 0.00424, considerably less than 0.1, and the validity value RSQ was 0.99998, greater than 0.60, which equals an excellent goodness of fit. The map plots each variable, thus permitting us to examine the similarity according to the variables’ proximity to each other. We labeled three dimensions, or categories, with each dimension implicating a potential factor.
The three clusters and their interpretations are as follows. (1) The first cluster contains B1 to B7 and B12. This cluster has 8 spots, and they are more interconnected. B4 and B7 occupy approximately the same coordinate. This cluster implicates a potential factor of 1, which we call a traditional metrics group because 7 out of 8 indicators in this cluster are citation and download indicators. (2) The second cluster contains B10, B11, and B14 to B20. This cluster has 9 spots, and they are more interconnected. B14 and B15 occupy approximately the same coordinate. This cluster implicates a potential factor of 2, and we call it the trackback, rating, note, and comment metrics group. (3) The third cluster contains B8, B9, and B13. This cluster has 3 spots, yet they are less interconnected, with more diverse networks. This cluster implicates a potential factor of 3, and we call it the blog and social bookmark metrics group.
We know that an MDS graph can represent the relations among nodes, while a network diagram can describe the social structure. Hence, we visualized the results of the nonmetric MDS from a network context with NetDraw (version 2.084, which is distributed with UCINET 6). The network diagram is shown in
A good drawing of a graph can immediately suggest some of the most important features of the overall network structure. The diagram indicates the following findings: (1) not all nodes are connected, as three nodes (B8, B9, and B13) that are disconnected from the others; (2) two subgroups or local “clusters” of actors are tied to each other, not to other groups, and (3) some actors have many ties, and some, few ties. Four nodes (B10, B16, B17, and B19) have two ties, while the other nodes have one tie or zero ties. These nodes are embedded in the neighborhood by the two clusters; that is, they are important for connecting the two clusters, which we call cluster 1 and cluster 2. Thus, examining the node and the “node network” (ie, “neighborhood”) indicates a sense of the structural constraints and opportunities that an actor faces and may help us to understand an actor’s role in a social structure. Finally, it indicates that (4) some difference in the strength of the relationship between a multivariable and its center remain. For example, B12 and B2 have a weak relationship with their center, while B6 and B20 have a relatively stronger relationship (that is, “1.0”) with their centers, and B17 has the strongest relationship (that is, “1.4”) with its center.
MDS diagram of altmetric indicators.
Social network structure diagram of altmetric indicators.
Our study is the first to use the MDS and network map to analyze the dimensions and interactions among altmetrics variables. Although MDS diagrams have been used for cocitation [
More importantly, we transformed the MDS diagram into a social network graph, whose advantage is that it displays the overall network structure. In research related to altmetrics, authors have developed coword social network maps for articles published in blogs [
What do these findings imply? There may be a transfer phenomenon for social impact of academic articles. Then,
Another finding is that altmetrics correlate with traditional measures significantly; that the citation and download metrics cluster closely together by the Spearman correlation method is consistent with previous results [
Our third contribution is the adoption of the theory of nonparametric testing throughout analysis. Based on the onesample KS test and the shapes of the histograms, we concluded that the distribution is significantly nonnormal and positively skewed. Priem summarized a group of histograms similarly but did not perform a nonparametric test to prove the abnormal distribution or to compute the skewness [
Our results also support that altmetric indicators may obey certain rules, for example, the Pareto law. Eysenbach [
Based on our experimental results, we conclude that altmetrics complements traditional statistics and contains approximately three dimensions: traditional, active, and inactive metrics. In summary, our study demonstrates a novel interaction among the altmetrics variables and analyzes articles’ social impact transfer mechanism.
Our conclusion that the distribution is significantly nonnormal and positively skewed rests primarily on the results obtained with the ArticleLevel Metrics dataset downloaded from PLOS API. Both Priem [
However, as alternative metrics indicators are preset in the dataset, the implication of our study’s findings is limited. This study was a preliminary attempt, and we are preparing to test and verify these findings for other types of datasets. The findings of correlations have been confirmed by another dataset concerning altmetric indicators in [
In conclusion, we studied the dimension and the structure of altmetrics with visual graphics. Our findings provide an important direction regarding the current practices of authors, editors, and academic administrations. Authors should pay more attention to the scholarly social impact that originates from active altmetrics and then participate more in related activities such as rating websites, noting, and commenting on articles. The publishers should attempt to launch an open peer review and consider scientific citizens’ perspectives before deciding whether to publish. They should also explore the value and the applications of postpublication interactivity in terms of ratings, notes, or comments. Academic administrations should track the dissemination of published articles (in terms of multiple types of citation, ratings, comments, and notes) and access uptodate altmetrics data to determine article quality or the impact context for tenure and promotion decisions.
application programming interface
KaiserMeyerOlkin
KolmogorovSmirnov Test
multidimensional scale
rsquared
The authors would like to acknowledge the contributions from Qi Ruiqun in figure editing, and help from Cara Bertozzi and his colleagues in manuscript editing and proofreading. Thanks also to the Public Library of Science for creating and maintaining ArticleLevel Metrics as a free and open data source, as well as all other providers of free altmetrics data.
None declared.