Search (4 results, page 1 of 1)

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Abstract

Twitter as a potential alternative source of external links for use in webometric analysis is analyzed because of its capacity to embed hyperlinks in different tweets. Given the limitations on searching Twitter's public application programming interface (API), we used the Topsy search engine as a source for compiling tweets. To this end, we took a global sample of 200 universities and compiled all the tweets with hyperlinks to any of these institutions. Further link data was obtained from alternative sources (MajesticSEO and OpenSiteExplorer) in order to compare the results. Thereafter, various statistical tests were performed to determine the correlation between the indicators and the possibility of predicting external links from the collected tweets. The results indicate a high volume of tweets, although they are skewed by the performance of specific universities and countries. The data provided by Topsy correlated significantly with all link indicators, particularly with OpenSiteExplorer (r?=?0.769). Finally, prediction models do not provide optimum results because of high error rates. We conclude that the use of Twitter (via Topsy) as a source of hyperlinks to universities produces promising results due to its high correlation with link indicators, though limited by policies and culture regarding use and presence in social networks.

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Abstract

Google Scholar has been well received by the research community. Its promises of free, universal, and easy access to scientific literature coupled with the perception that it covers the social sciences and the humanities better than other traditional multidisciplinary databases have contributed to the quick expansion of Google Scholar Citations and Google Scholar Metrics: 2 new bibliometric products that offer citation data at the individual level and at journal level. In this article, we show the results of an experiment undertaken to analyze Google Scholar's capacity to detect citation-counting manipulation. For this, we uploaded 6 documents to an institutional web domain that were authored by a fictitious researcher and referenced all the publications of the members of the EC3 research group at the University of Granada. The detection by Google Scholar of these papers caused an outburst in the number of citations included in the Google Scholar Citations profiles of the authors. We discuss the effects of such an outburst and how it could affect the future development of such products, at both the individual level and the journal level, especially if Google Scholar persists with its lack of transparency.

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Abstract

We present an analysis of data citation practices based on the Data Citation Index (DCI) (Thomson Reuters). This database launched in 2012 links data sets and data studies with citations received from the other citation indexes. The DCI harvests citations to research data from papers indexed in the Web of Science. It relies on the information provided by the data repository. The findings of this study show that data citation practices are far from common in most research fields. Some differences have been reported on the way researchers cite data: Although in the areas of science and engineering & technology data sets were the most cited, in the social sciences and arts & humanities data studies play a greater role. A total of 88.1% of the records have received no citation, but some repositories show very low uncitedness rates. Although data citation practices are rare in most fields, they have expanded in disciplines such as crystallography and genomics. We conclude by emphasizing the role that the DCI could play in encouraging the consistent, standardized citation of research data-a role that would enhance their value as a means of following the research process from data collection to publication.

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Date

20. 1.2015 18:30:22