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ABSTRACT This study investigates the effect of diffusing scientific articles on Twitter on their citation impact. For a set of 1.3 million papers covered by the Web of Science and published in 2012, normalized citation rates are compared between tweeted and non-tweeted articles published in the same journal. The results indicate that tweeted papers, published in the same year and in the same journal received around 30% higher citation rates than papers which were not diffused on Twitter. This type of citation advantage appeared across disciplines and countries of authors, but varied in extent. [ABSTRACT FROM AUTHOR]

ABSTRACT There have been many studies on the factors influencing paper citation counts. A number of studies have focused on the citing papers, and corresponding methods were proposed to measure the prestige of citations based on the journal impact factors, the total citation counts and the PageRank algorithm values. However, there are drawbacks to these methods. In this paper, we propose a novel method to identify prestigious citations from the affiliation of the citing paper. Specifically, if the authors of the citing paper are affiliated with a prestigious university, the citing paper could be counted as a prestigious citation. As a pilot, we used the top 200 universities on the QS World University Rankings 2015 to identify the prestigious universities so that the prestigious citations, named as QS citations, were identified accordingly. Experimental results validated that QS citations have more important impact on the cited papers than other citations. Papers with QS citations have better performance across most disciplines not only in total citation counts, but also in altmetrics such as the Altmetric Attension Score and Mendeley reader counts. [ABSTRACT FROM AUTHOR]

Taking international LIS journals as an example, combining 8 traditional citation indicators and 10 altmetrics indicators, we use the method of multi‐indicator fusion to construct a comprehensive evaluation model of journal impact based on traditional citation indicators and altmetrics indicators. Through normalization processing, correlation analysis, reliability analysis, validity analysis, factor analysis, the paper evaluates the academic impact (FA), societal impact (FS) of LIS journals respectively. We get the following conclusion: (1) We get the 2‐dimensional evaluation results and according to LIS journals impacts, we divide the roles into four categories: "Prestige journals", "Star journals", "Common journals" and "Expert journals". (2) It is found that 8 traditional citation indicators based on JCR, present strong positive correlations, and the indicators are highly consistent overall and internally. (3) There are moderate or high positive correlations among 10 altmetrics based on Altmetrics Explorer, and there are also significant consistency among the indicators as a whole and internally. (4) The correlation coefficient between FA and FS is 0.566, with a moderate positive correlation. It indicates that the evaluation of LIS journals' societal impact based on altmetrics indicators has a useful supplement to the evaluation of academic impact based on citation. [ABSTRACT FROM AUTHOR]

It has always been argued that social sciences and humanities (SSH) impact evaluation calls for a broader range of indicators than only bibliometric indicators. This study analyzes the performance of scientific SSH publications using 1,327,924 records from the Social Sciences Citation Index (SSCI) and the Arts & Humanities Citation Index (A&HCI), and 629,586 records from Altmetric.com. The research covers a period of 5 years (2013–2017); three publication types (articles, reviews, and proceedings papers); four selected subject fields; three citation indicators; and 13 altmetric indicators. The comparative analysis of SSH performance is studied in both citation and altmetric pattern, and an attempt is made to interpret the results in both the metric and discipline contexts. Major contributions of this study include the extension of analysis methods for the SSH, as well as the validation of altmetric indicators. [ABSTRACT FROM AUTHOR]

Using Article-Level-Metrics (ALMs) data from PLOS, we analyze the citations, views, saves, and shares of different types of articles. Using multiple statistical and visualization techniques, we explore scientists' behaviors associated with scholarly works, which could help us better understand their information needs, and provide clues about how different types of scholarly information are valued. We find that scholarly impact could be better interpreted when article types are taken into consideration. Moreover, the correlations between citations, views, saves, and shares also demonstrate the need to consider article types when assessing the validity of altmetrics. Since altmetrics usage data are associated with various information behaviors, we advocate that altmetrics, especially only the scores, should be used with caution. In summary, our approach provides a novel point of view to explore altmetrics and citation data. Future work will adopt quality approaches to look into more details of the current analyses, to get a better understanding of flavors of scholarly impact. [ABSTRACT FROM AUTHOR]

We examined the correlations between an article's citation count (as an indicator of the article's quality) and different impact measures regarding the article's references (as indicators of the quality of the authors' information seeking while producing the article). Two research questions of interest were studied. First, do authors' information seeking and use affect the quality of their research output? Second, if yes, which metrics can better identify those references that may inspire researchers to produce high quality research work (so that we can recommend these references to others)? We found that the impact measures for the references have a slight or moderate correlation with article citations. Among these measures, references' article level impact measures calculated based on altmetrics data sources have a consistently stronger correlation with high quality research output, which may serve to identify helpful articles for researchers. [ABSTRACT FROM AUTHOR]