Empirical Patterns in Google Scholar Citation Counts

Scholarly impact may be metricized using an author's total number of citations as a stand-in for real worth, but this measure varies in applicability between disciplines. The detail of the number of citations per publication is nowadays mapped in much more detail on the Web, exposing certain empirical patterns. This paper explores those patterns, using the citation data from Google Scholar for a number of authors.Comment: 6 pages, 8 figures, submitted to Cyberpatterns 201

Applying weighted PageRank to author citation networks

This paper aims to identify whether different weighted PageRank algorithms can be applied to author citation networks to measure the popularity and prestige of a scholar from a citation perspective. Information Retrieval (IR) was selected as a test field and data from 1956-2008 were collected from Web of Science (WOS). Weighted PageRank with citation and publication as weighted vectors were calculated on author citation networks. The results indicate that both popularity rank and prestige rank were highly correlated with the weighted PageRank. Principal Component Analysis (PCA) was conducted to detect relationships among these different measures. For capturing prize winners within the IR field, prestige rank outperformed all the other measures.Comment: 19 pages, 4 figures, 5 table

Microsoft Academic automatic document searches: accuracy for journal articles and suitability for citation analysis

Microsoft Academic is a free academic search engine and citation index that is similar to Google Scholar but can be automatically queried. Its data is potentially useful for bibliometric analysis if it is possible to search effectively for individual journal articles. This article compares different methods to find journal articles in its index by searching for a combination of title, authors, publication year and journal name and uses the results for the widest published correlation analysis of Microsoft Academic citation counts for journal articles so far. Based on 126,312 articles from 323 Scopus subfields in 2012, the optimal strategy to find articles with DOIs is to search for them by title and filter out those with incorrect DOIs. This finds 90% of journal articles. For articles without DOIs, the optimal strategy is to search for them by title and then filter out matches with dissimilar metadata. This finds 89% of journal articles, with an additional 1% incorrect matches. The remaining articles seem to be mainly not indexed by Microsoft Academic or indexed with a different language version of their title. From the matches, Scopus citation counts and Microsoft Academic counts have an average Spearman correlation of 0.95, with the lowest for any single field being 0.63. Thus, Microsoft Academic citation counts are almost universally equivalent to Scopus citation counts for articles that are not recent but there are national biases in the results

Do open citations inform the qualitative peer-review evaluation in research assessments? An analysis of the Italian National Scientific Qualification

In the past, several works have investigated ways for combining quantitative and qualitative methods in research assessment exercises. Indeed, the Italian National Scientific Qualification (NSQ), i.e. the national assessment exercise which aims at deciding whether a scholar can apply to professorial academic positions as Associate Professor and Full Professor, adopts a quantitative and qualitative evaluation process: it makes use of bibliometrics followed by a peer-review process of candidates' CVs. The NSQ divides academic disciplines into two categories, i.e. citation-based disciplines (CDs) and non-citation-based disciplines (NDs), a division that affects the metrics used for assessing the candidates of that discipline in the first part of the process, which is based on bibliometrics. In this work, we aim at exploring whether citation-based metrics, calculated only considering open bibliographic and citation data, can support the human peer-review of NDs and yield insights on how it is conducted. To understand if and what citation-based (and, possibly, other) metrics provide relevant information, we created a series of machine learning models to replicate the decisions of the NSQ committees. As one of the main outcomes of our study, we noticed that the strength of the citational relationship between the candidate and the commission in charge of assessing his/her CV seems to play a role in the peer-review phase of the NSQ of NDs

COVID-19 publications: Database coverage, citations, readers, tweets, news, Facebook walls, Reddit posts

© 2020 The Authors. Published by MIT Press. This is an open access article available under a Creative Commons licence. The published version can be accessed at the following link on the publisher’s website: https://doi.org/10.1162/qss_a_00066The COVID-19 pandemic requires a fast response from researchers to help address biological, medical and public health issues to minimize its impact. In this rapidly evolving context, scholars, professionals and the public may need to quickly identify important new studies. In response, this paper assesses the coverage of scholarly databases and impact indicators during 21 March to 18 April 2020. The rapidly increasing volume of research, is particularly accessible through Dimensions, and less through Scopus, the Web of Science, and PubMed. Google Scholar’s results included many false matches. A few COVID-19 papers from the 21,395 in Dimensions were already highly cited, with substantial news and social media attention. For this topic, in contrast to previous studies, there seems to be a high degree of convergence between articles shared in the social web and citation counts, at least in the short term. In particular, articles that are extensively tweeted on the day first indexed are likely to be highly read and relatively highly cited three weeks later. Researchers needing wide scope literature searches (rather than health focused PubMed or medRxiv searches) should start with Dimensions (or Google Scholar) and can use tweet and Mendeley reader counts as indicators of likely importance