3 results
Ageing, old age and older adults: a social media analysis of dominant topics and discourses
- Meiko Makita, Amalia Mas-Bleda, Emma Stuart, Mike Thelwall
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- Journal:
- Ageing & Society / Volume 41 / Issue 2 / February 2021
- Published online by Cambridge University Press:
- 13 August 2019, pp. 247-272
- Print publication:
- February 2021
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- Article
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Whilst representations of old age and older people in traditional media have been well documented, examinations of such representations within social media discourse are still scarce. This is an unfortunate omission because of the importance of social media for communication in contemporary society. In this study, we combine content analysis and discourse analysis to explore patterns of representation on Twitter around the terms ageing, old age, older people and elderly with a sample of 1,200 tweets. Our analysis shows that ‘personal concerns/views’ and ‘health and social care’ are the predominant overall topics, although some topics are clearly linked with specific keywords. The language often used in the tweets seems to reinforce negative discourses of age and ageing that locate older adults as a disempowered, vulnerable and homogeneous group; old age is deemed a problem and ageing is considered something that needs to be resisted, slowed or disguised. These topics and discursive patterns are indeed similar to those found in empirical studies of social perceptions and traditional media portrayal of old age, which indicates that social media and Twitter in particular appears to serve as an online platform that reproduces and reinforces existing ageist discourses in traditional media that feed into social perceptions of ageing and older people.
7 - Assessing web search engines: a webometric approach
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- By Mike Thelwall, University of Wolverhampton, UK
- Edited by Allen Foster, Pauline Rafferty
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- Book:
- Innovations in Information Retrieval
- Published by:
- Facet
- Published online:
- 08 June 2018
- Print publication:
- 31 July 2011, pp 135-146
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- Chapter
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Summary
Introduction
Information Retrieval (IR) research typically evaluates search systems in terms of the standard precision, recall and F-measures to weight the relative importance of precision and recall (e.g. van Rijsbergen, 1979). All of these assess the extent to which the system returns good matches for a query. In contrast, webometric measures are designed specifically for web search engines and are designed to monitor changes in results over time and various aspects of the internal logic of the way in which search engine select the results to be returned. This chapter introduces a range of webometric measurements and illustrates them with case studies of Google, Bing and Yahoo! This is a very fertile area for simple and complex new investigations into search engine results.
The modern commercial web search engine is a highly complex system (Arasu et al., 2001) with vast social and commercial significance (Van Couvering, 2004, 2007). Although they can be evaluated to some extent with traditional IR measures like precision and recall, web search engines behave differently from traditional IR systems in many respects. Three key differences are the importance of rank order in the results; the limitation to 1,000 matches per query; and the goal of delivering relevant and useful results rather than technically accurate matches (in the sense of: Bar-Ilan and Peritz, 2008). In response, evaluation metrics have been developed to measure new characteristics of web search engines, such as the effectiveness of the rank order of the results (Zaragoza, Cambazoglu and Baeza-Yates, 2010), mean average precision (Turpin and Scholer, 2006) and discounted cumulative gain (Jarvelin and Kekalainen, 2002). A broad common goal is to assess the extent to which any web search engine delivers good and relevant results to users. For some information scientists, however, IR goals are not sufficient with regard to web search engines, for two reasons.
First, search engines like Google are so important in academia and daily life that it is important for information professionals to understand something of how they work and what their limitations are.
15 - Bibliometrics to webometrics
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- By Mike Thelwall, University of Wolverhampton, UK
- Edited by Alan Gilchrist
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- Book:
- Information Science in Transition
- Published by:
- Facet
- Published online:
- 08 June 2018
- Print publication:
- 30 April 2009, pp 347-376
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- Chapter
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Summary
Abstract
Bibliometrics has changed out of all recognition since 1958; becoming established as a field, being taught widely in library and information science schools, and being at the core of a number of science evaluation research groups around the world. This was all made possible by the work of Eugene Garfield and his Science Citation Index. This article reviews the distance that bibliometrics has travelled since 1958 by comparing early bibliometrics with current practice, and by giving an overview of a range of recent developments, such as patent analysis, national research evaluation exercises, visualization techniques, new applications, online citation indexes, and the creation of digital libraries. Webometrics, a modern, fast-growing offshoot of bibliometrics, is reviewed in detail. Finally, future prospects are discussed with regard to both bibliometrics and webometrics.
Introduction
The last 50 years have seen two major technological changes in scholarly publishing and two major changes in the way research can be quantitatively analysed, alongside numerous less significant developments. The two publishing changes are the computerization of the printing process, reducing costs significantly and allowing more journals and books to appear in print; and the conversion of the entire publishing cycle (submission of articles, refereeing and publication) to the internet, allowing faster and possibly cheaper communication throughout. Historically, the first major change for the development of quantitative analysis of academic publishing (bibliometrics) was the creation of the Institute for Scientific Information (ISI, now Thomson Scientific) citation database, which began functioning in 1962 [1, 2] together with associated post-war sociological theory allowing it to be used to assess the impact of scientific work [3]. Since then there has been a continuous increase in the computing power available in universities, which has helped to make increasing numbers of bibliometric analyses possible. The second major development for bibliometrics was the web publishing of an increasingly broad range of research-related documents, from articles to email discussion lists, allowing the creation of a range of new metrics relating to their access and use.
In this article, the focus is on the measurement of science.