Search (3 results, page 1 of 1)

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Abstract

Much has been written about the potential and pitfalls of macroscopic Web-based link analysis, yet there have been no studies that have provided clear statistical evidence that any of the proposed calculations can produce results over large areas of the Web that correlate with phenomena external to the Internet. This article attempts to provide such evidence through an evaluation of Ingwersen's (1998) proposed external Web Impact Factor (WIF) for the original use of the Web: the interlinking of academic research. In particular, it studies the case of the relationship between academic hyperlinks and research activity for universities in Britain, a country chosen for its variety of institutions and the existence of an official government rating exercise for research. After reviewing the numerous reasons why link counts may be unreliable, it demonstrates that four different WIFs do, in fact, correlate with the conventional academic research measures. The WIF delivering the greatest correlation with research rankings was the ratio of Web pages with links pointing at research-based pages to faculty numbers. The scarcity of links to electronic academic papers in the data set suggests that, in contrast to citation analysis, this WIF is measuring the reputations of universities and their scholars, rather than the quality of their publications

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Abstract

In both the social sciences and the humanities, books and monographs play significant roles in research communication. The absence of citations from most books and monographs from the Thomson Reuters/Institute for Scientific Information databases (ISI) has been criticized, but attempts to include citations from or to books in the research evaluation of the social sciences and humanities have not led to widespread adoption. This article assesses whether Google Book Search (GBS) can partially fill this gap by comparing citations from books with citations from journal articles to journal articles in 10 science, social science, and humanities disciplines. Book citations were 31% to 212% of ISI citations and, hence, numerous enough to supplement ISI citations in the social sciences and humanities covered, but not in the sciences (3%-5%), except for computing (46%), due to numerous published conference proceedings. A case study was also made of all 1,923 articles in the 51 information science and library science ISI-indexed journals published in 2003. Within this set, highly book-cited articles tended to receive many ISI citations, indicating a significant relationship between the two types of citation data, but with important exceptions that point to the additional information provided by book citations. In summary, GBS is clearly a valuable new source of citation data for the social sciences and humanities. One practical implication is that book-oriented scholars should consult it for additional citations to their work when applying for promotion and tenure.

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Abstract

Webometrics, the quantitative study of Web-related phenomena, emerged from the realization that methods originally designed for bibliometric analysis of scientific journal article citation patterns could be applied to the Web, with commercial search engines providing the raw data. Almind and Ingwersen (1997) defined the field and gave it its name. Other pioneers included Rodriguez Gairin (1997) and Aguillo (1998). Larson (1996) undertook exploratory link structure analysis, as did Rousseau (1997). Webometrics encompasses research from fields beyond information science such as communication studies, statistical physics, and computer science. In this review we concentrate on link analysis, but also cover other aspects of webometrics, including Web log fle analysis. One theme that runs through this chapter is the messiness of Web data and the need for data cleansing heuristics. The uncontrolled Web creates numerous problems in the interpretation of results, for instance, from the automatic creation or replication of links. The loose connection between top-level domain specifications (e.g., com, edu, and org) and their actual content is also a frustrating problem. For example, many .com sites contain noncommercial content, although com is ostensibly the main commercial top-level domain. Indeed, a skeptical researcher could claim that obstacles of this kind are so great that all Web analyses lack value. As will be seen, one response to this view, a view shared by critics of evaluative bibliometrics, is to demonstrate that Web data correlate significantly with some non-Web data in order to prove that the Web data are not wholly random. A practical response has been to develop increasingly sophisticated data cleansing techniques and multiple data analysis methods.