Usage Bibliometrics

Scholarly usage data provides unique opportunities to address the known shortcomings of citation analysis. However, the collection, processing and analysis of usage data remains an area of active research. This article provides a review of the state-of-the-art in usage-based informetric, i.e. the use of usage data to study the scholarly process.Comment: Publisher's PDF (by permission). Publisher web site: books.infotoday.com/asist/arist44.shtm

Comparing People with Bibliometrics

Bibliometric indicators, citation counts and/or download counts are increasingly being used to inform personnel decisions such as hiring or promotions. These statistics are very often misused. Here we provide a guide to the factors which should be considered when using these so-called quantitative measures to evaluate people. Rules of thumb are given for when begin to use bibliometric measures when comparing otherwise similar candidates.Comment: to appear in Proceedings of Library and Information Science in Astronomy VIII (LISA-8

Social reference: Aggregating online usage of scientific literature in CiteULike for clustering academic resources

Citation-based methods have been widely studied and employed for clustering academic resources and mapping science. Although effective, these methods suffer from citation delay. In this study, we extend reference and citation analysis to a broader notion from social perspective. We coin the term "social reference" to refer to the references of literatures in social academic web environment. We propose clustering methods using social reference information from CiteULike. We experiment for journal clustering and author clustering using social reference and compare with citation-based methods. Our experiments indicate: first, social reference implies connections among literatures which are as effective as citation in clustering academic resources; second, in practical settings, social reference-based clustering methods are not as effective as citation-based ones due to the sparseness of social reference data, but they can outperform in clustering new resources that have few citation. © 2011 Authors

How the Scientific Community Reacts to Newly Submitted Preprints: Article Downloads, Twitter Mentions, and Citations

We analyze the online response to the preprint publication of a cohort of 4,606 scientific articles submitted to the preprint database arXiv.org between October 2010 and May 2011. We study three forms of responses to these preprints: downloads on the arXiv.org site, mentions on the social media site Twitter, and early citations in the scholarly record. We perform two analyses. First, we analyze the delay and time span of article downloads and Twitter mentions following submission, to understand the temporal configuration of these reactions and whether one precedes or follows the other. Second, we run regression and correlation tests to investigate the relationship between Twitter mentions, arXiv downloads and article citations. We find that Twitter mentions and arXiv downloads of scholarly articles follow two distinct temporal patterns of activity, with Twitter mentions having shorter delays and narrower time spans than arXiv downloads. We also find that the volume of Twitter mentions is statistically correlated with arXiv downloads and early citations just months after the publication of a preprint, with a possible bias that favors highly mentioned articles.Comment: 15 pages, 7 Figures, 3 Tables. PLoS One, in pres