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Abstract

Purpose The purpose of this paper is to analyze the relationship between dissemination of research papers on Twitter and its influence on research impact. Design/methodology/approach Four types of journal Twitter accounts (journal, owner, publisher and no Twitter account) were defined to observe differences in the number of tweets and citations. In total, 4,176 articles from 350 journals were extracted from Plum Analytics. This altmetric provider tracks the number of tweets and citations for each paper. Student's t-test for two-paired samples was used to detect significant differences between each group of journals. Regression analysis was performed to detect which variables may influence the getting of tweets and citations. Findings The results show that journals with their own Twitter account obtain more tweets (46 percent) and citations (34 percent) than journals without a Twitter account. Followers is the variable that attracts more tweets (ß=0.47) and citations (ß=0.28) but the effect is small and the fit is not good for tweets (R2=0.46) and insignificant for citations (R2=0.18). Originality/value This is the first study that tests the performance of research journals on Twitter according to their handles, observing how the dissemination of content in this microblogging network influences the citation of their papers.

Date

20. 1.2015 18:30:22

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Abstract

This paper introduces a keyword map of the labels used by the scientists registered in the Google Scholar Citations (GSC) database from December 2011. In all, 15,000 random queries were formulated to GSC to obtain a list of 26,682 registered users. From this list a network graph of 6,660 labels was built and classified according to the Scopus Subject Area classes. Results display a detailed label map of the most used (>15 times) tags. The structural analysis shows that the core of the network is occupied by computer science-related disciplines that account for the most used and shared labels. This core is surrounded by clusters of disciplines related or close to computing such as Information Sciences, Mathematics, or Bioinformatics. Classical areas such as Chemistry and Physics are marginalized in the graph. It is suggested that GSC would in the future be an accurate source to map Science because it is based on the labels that scientists themselves use to describe their own research activity.