Search (83 results, page 1 of 5)

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Abstract

Webometrics is an information science field concerned with measuring aspects of the World Wide Web (WWW) for a variety of information science research goals. It came into existence about five years after the Web was formed and has since grown to become a significant aspect of information science, at least in terms of published research. Although some webometrics research has focused on the structure or evolution of the Web itself or the performance of commercial search engines, most has used data from the Web to shed light on information provision or online communication in various contexts. Most prominently, techniques have been developed to track, map, and assess Web-based informal scholarly communication, for example, in terms of the hyperlinks between academic Web sites or the online impact of digital repositories. In addition, a range of nonacademic issues and groups of Web users have also been analyzed.

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Abstract

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.

Source

Innovations in information retrieval: perspectives for theory and practice. Eds.: A. Foster, u. P. Rafferty

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Abstract

The quality and impact of academic Web sites is of interest to many audiences, including the scholars who use them and Web educators who need to identify best practice. Several large-scale European Union research projects have been funded to build new indicators for online scientific activity, reflecting recognition of the importance of the Web for scholarly communication. In this paper we address the key question of whether higher rated scholars produce higher impact Web sites, using the United Kingdom as a case study and measuring scholars' quality in terms of university-wide average research ratings. Methodological issues concerning the measurement of the online impact are discussed, leading to the adoption of counts of links to a university's constituent single domain Web sites from an aggregated counting metric. The findings suggest that universities with higher rated scholars produce significantly more Web content but with a similar average online impact. Higher rated scholars therefore attract more total links from their peers, but only by being more prolific, refuting earlier suggestions. It can be surmised that general Web publications are very different from scholarly journal articles and conference papers, for which scholarly quality does associate with citation impact. This has important implications for the construction of new Web indicators, for example that online impact should not be used to assess the quality of small groups of scholars, even within a single discipline.

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Abstract

Webometrics, the quantitative study of Web phenomena, is a field encompassing contributions from information science, computer science, and statistical physics. Its methodology draws especially from bibliometrics. This special issue presents contributions that both push for ward the field and illustrate a wide range of webometric approaches.

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Abstract

Introduces several new versions of PageRank (the link based Web page ranking algorithm), based on an information science perspective on the concept of the Web document. Although the Web page is the typical indivisible unit of information in search engine results and most Web information retrieval algorithms, other research has suggested that aggregating pages based on directories and domains gives promising alternatives, particularly when Web links are the object of study. The new algorithms introduced based on these alternatives were used to rank four sets of Web pages. The ranking results were compared with human subjects' rankings. The results of the tests were somewhat inconclusive: the new approach worked well for the set that includes pages from different Web sites; however, it does not work well in ranking pages that are from the same site. It seems that the new algorithms may be effective for some tasks but not for others, especially when only low numbers of links are involved or the pages to be ranked are from the same site or directory.

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Abstract

Sentiment analysis is concerned with the automatic extraction of sentiment-related information from text. Although most sentiment analysis addresses commercial tasks, such as extracting opinions from product reviews, there is increasing interest in the affective dimension of the social web, and Twitter in particular. Most sentiment analysis algorithms are not ideally suited to this task because they exploit indirect indicators of sentiment that can reflect genre or topic instead. Hence, such algorithms used to process social web texts can identify spurious sentiment patterns caused by topics rather than affective phenomena. This article assesses an improved version of the algorithm SentiStrength for sentiment strength detection across the social web that primarily uses direct indications of sentiment. The results from six diverse social web data sets (MySpace, Twitter, YouTube, Digg, Runners World, BBC Forums) indicate that SentiStrength 2 is successful in the sense of performing better than a baseline approach for all data sets in both supervised and unsupervised cases. SentiStrength is not always better than machine-learning approaches that exploit indirect indicators of sentiment, however, and is particularly weaker for positive sentiment in news-related discussions. Overall, the results suggest that, even unsupervised, SentiStrength is robust enough to be applied to a wide variety of different social web contexts.

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Abstract

Word usage is of interest to linguists for its own sake as well as to social scientists and others who seek to track the spread of ideas, for example, in public debates over political decisions. The historical evolution of language can be analyzed with the tools of corpus linguistics through evolving corpora and the Web. But word usage statistics can only be gathered for known words. In this article, techniques are described and tested for identifying new words from the Web, focusing on the case when the words are related to a topic and have a hybrid form with a common sequence of letters. The results highlight the need to employ a combination of search techniques and show the wide potential of hybrid word family investigations in linguistics and social science.

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Abstract

A common task in both Webmetrics and Web information retrieval is to identify a set of Web pages or sites that are similar in content. In this paper we assess the extent to which links, colinks and couplings can be used to identify similar Web sites. As an experiment, a random sample of 500 pairs of domains from the UK academic Web were taken and human assessments of site similarity, based upon content type, were compared against ratings for the three concepts. The results show that using a combination of all three gives the highest probability of identifying similar sites, but surprisingly this was only a marginal improvement over using links alone. Another unexpected result was that high values for either colink counts or couplings were associated with only a small increased likelihood of similarity. The principal advantage of using couplings and colinks was found to be greater coverage in terms of a much larger number of pairs of sites being connected by these measures, instead of increased probability of similarity. In information retrieval terminology, this is improved recall rather than improved precision.

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Abstract

Purpose - The purpose of this paper is to claim that it is useful to assess the web connectivity of research groups, describe hyperlink-based techniques to achieve this and present brief details of European life sciences research groups as a case study. Design/methodology/approach - A commercial search engine was harnessed to deliver hyperlink data via its automatic query submission interface. A special purpose link analysis tool, LexiURL, then summarised and graphed the link data in appropriate ways. Findings - Webometrics can provide a wide range of descriptive information about the international connectivity of research groups. Research limitations/implications - Only one field was analysed, data was taken from only one search engine, and the results were not validated. Practical implications - Web connectivity seems to be particularly important for attracting overseas job applicants and to promote research achievements and capabilities, and hence we contend that it can be useful for national and international governments to use webometrics to ensure that the web is being used effectively by research groups. Originality/value - This is the first paper to make a case for the value of using a range of webometric techniques to evaluate the web presences of research groups within a field, and possibly the first "applied" webometrics study produced for an external contract.

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Abstract

Google's PageRank is an influential algorithm that uses a model of Web use that is dominated by its link structure in order to rank pages by their estimated value to the Web community. This paper reports on the outcome of applying the algorithm to the Web sites of three national university systems in order to test whether it is capable of identifying the most important Web pages. The results are also compared with simple inlink counts. It was discovered that the highest inlinked pages do not always have the highest PageRank, indicating that the two metrics are genuinely different, even for the top pages. More significantly, however, internal links dominated external links for the high ranks in either method and superficial reasons accounted for high scores in both cases. It is concluded that PageRank is not useful for identifying the top pages in a site and that it must be combined with a powerful text matching techniques in order to get the quality of information retrieval results provided by Google.

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Abstract

In recent years there has been an increasing demand for research evaluation within universities and other research-based organisations. In parallel, there has been an increasing recognition that traditional citation-based indicators are not able to reflect the societal impacts of research and are slow to appear. This has led to the creation of new indicators for different types of research impact as well as timelier indicators, mainly derived from the Web. These indicators have been called altmetrics, webometrics or just web metrics. This book describes and evaluates a range of web indicators for aspects of societal or scholarly impact, discusses the theory and practice of using and evaluating web indicators for research assessment and outlines practical strategies for obtaining many web indicators. In addition to describing impact indicators for traditional scholarly outputs, such as journal articles and monographs, it also covers indicators for videos, datasets, software and other non-standard scholarly outputs. The book describes strategies to analyse web indicators for individual publications as well as to compare the impacts of groups of publications. The practical part of the book includes descriptions of how to use the free software Webometric Analyst to gather and analyse web data. This book is written for information science undergraduate and Master?s students that are learning about alternative indicators or scientometrics as well as Ph.D. students and other researchers and practitioners using indicators to help assess research impact or to study scholarly communication.

Series

Synthesis lectures on information concepts, retrieval, and services; 52

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Abstract

A layered approach for identifying communities in the Web is presented and explored by applying the flake exact community identification algorithm to the UK academic Web. Although community or topic identification is a common task in information retrieval, a new perspective is developed by: the application of alternative document models, shifting the focus from individual pages to aggregated collections based upon Web directories, domains and entire sites; the removal of internal site links; and the adaptation of a new fast algorithm to allow fully-automated community identification using all possible single starting points. The overall topology of the graphs in the three least-aggregated layers was first investigated and found to include a large number of isolated points but, surprisingly, with most of the remainder being in one huge connected component, exact proportions varying by layer. The community identification process then found that the number of communities far exceeded the number of topological components, indicating that community identification is a potentially useful technique, even with random starting points. Both the number and size of communities identified was dependent on the parameter of the algorithm, with very different results being obtained in each case. In conclusion, the UK academic Web is embedded with layers of non-trivial communities and, if it is not unique in this, then there is the promise of improved results for information retrieval algorithms that can exploit this additional structure, and the application of the technique directly to partially automate Web metrics tasks such as that of finding all pages related to a given subject hosted by a single country's universities.

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Abstract

Although the analysis of citations in the scholarly literature is now an established and relatively well understood part of information science, not enough is known about citations that can be found on the Web. In particular, are there new Web types, and if so, are these trivial or potentially useful for studying or evaluating research communication? We sought evidence based upon a sample of 1,577 Web citations of the URLs or titles of research articles in 64 open-access journals from biology, physics, chemistry, and computing. Only 25% represented intellectual impact, from references of Web documents (23%) and other informal scholarly sources (2%). Many of the Web/URL citations were created for general or subject-specific navigation (45%) or for self-publicity (22%). Additional analyses revealed significant disciplinary differences in the types of Google unique Web/URL citations as well as some characteristics of scientific open-access publishing on the Web. We conclude that the Web provides access to a new and different type of citation information, one that may therefore enable us to measure different aspects of research, and the research process in particular; but to obtain good information, the different types should be separated.

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Abstract

Despite over 10 years of research there is no agreement on the most suitable roles for Webometric indicators in support of research policy and almost no field-based Webometrics. This article partly fills these gaps by analyzing the potential of policy-relevant Webometrics for individual scientific fields with the help of 4 case studies. Although Webometrics cannot provide robust indicators of knowledge flows or research impact, it can provide some evidence of networking and mutual awareness. The scope of Webometrics is also relatively wide, including not only research organizations and firms but also intermediary groups like professional associations, Web portals, and government agencies. Webometrics can, therefore, provide evidence about the research process to compliment peer review, bibliometric, and patent indicators: tracking the early, mainly prepublication development of new fields and research funding initiatives, assessing the role and impact of intermediary organizations and the need for new ones, and monitoring the extent of mutual awareness in particular research areas.

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Abstract

Web links have been studied by information scientists for at least six years but it is only in the past two that clear evidence has emerged to show that counts of links to scholarly Web spaces (universities and departments) can correlate significantly with research measures, giving some credence to their use for the investigation of scholarly communication. This paper reports an a study to investigate the factors that influence the creation of links to journal Web sites. An empirical approach is used: collecting data and testing for significant patterns. The specific questions addressed are whether site age and site content are inducers of links to a journal's Web site as measured by the ratio of link counts to Journal Impact Factors, two variables previously discovered to be related. A new methodology for data collection is also introduced that uses the Internet Archive to obtain an earliest known creation date for Web sites. The results show that both site age and site content are significant factors for the disciplines studied: library and information science, and law. Comparisons between the two fields also show disciplinary differences in Web site characteristics. Scholars and publishers should be particularly aware that richer content an a journal's Web site tends to generate links and thus the traffic to the site.

Source

Journal of the American Society for Information Science and technology. 54(2003) no.1, S.29-38

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Abstract

A huge number of informal messages are posted every day in social network sites, blogs, and discussion forums. Emotions seem to be frequently important in these texts for expressing friendship, showing social support or as part of online arguments. Algorithms to identify sentiment and sentiment strength are needed to help understand the role of emotion in this informal communication and also to identify inappropriate or anomalous affective utterances, potentially associated with threatening behavior to the self or others. Nevertheless, existing sentiment detection algorithms tend to be commercially oriented, designed to identify opinions about products rather than user behaviors. This article partly fills this gap with a new algorithm, SentiStrength, to extract sentiment strength from informal English text, using new methods to exploit the de facto grammars and spelling styles of cyberspace. Applied to MySpace comments and with a lookup table of term sentiment strengths optimized by machine learning, SentiStrength is able to predict positive emotion with 60.6% accuracy and negative emotion with 72.8% accuracy, both based upon strength scales of 1-5. The former, but not the latter, is better than baseline and a wide range of general machine learning approaches.

Date

22. 1.2011 14:29:23

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Abstract

Some curricula vitae (web CVs) of academics on the web, including homepages and publication lists, link to open-access (OA) articles, resources, abstracts in publishers' websites, or academic discussions, helping to disseminate research. To assess how common such practices are and whether they vary by discipline, gender, and country, the authors conducted a large-scale e-mail survey of astronomy and astrophysics, public health, environmental engineering, and philosophy across 15 European countries and analyzed hyperlinks from web CVs of academics. About 60% of the 2,154 survey responses reported having a web CV or something similar, and there were differences between disciplines, genders, and countries. A follow-up outlink analysis of 2,700 web CVs found that a third had at least one outlink to an OA target, typically a public eprint archive or an individual self-archived file. This proportion was considerably higher in astronomy (48%) and philosophy (37%) than in environmental engineering (29%) and public health (21%). There were also differences in linking to publishers' websites, resources, and discussions. Perhaps most important, however, the amount of linking to OA publications seems to be much lower than allowed by publishers and journals, suggesting that many opportunities for disseminating full-text research online are being missed, especially in disciplines without established repositories. Moreover, few academics seem to be exploiting their CVs to link to discussions, resources, or article abstracts, which seems to be another missed opportunity for publicizing research.

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Abstract

A feature of modern democracies is public mistrust of scientists and the politicization of science policy, e.g., concerning stem cell research and genetically modified food. While the extent of this mistrust is debatable, its political influence is tangible. Hence, science policy researchers and science policy makers need early warning of issues that resonate with a wide public so that they can make timely and informed decisions. In this article, a semi-automatic method for identifying significant public science-related concerns from a corpus of Internet-based RSS (Really Simple Syndication) feeds is described and shown to be an improvement on a previous similar system because of the introduction of feedbased aggregation. In addition, both the RSS corpus and the concept of public science-related fears are deconstructed, revealing hidden complexity. This article also provides evidence that genetically modified organisms and stem cell research were the two major policyrelevant science concern issues, although mobile phone radiation and software security also generated significant interest.

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Abstract

Commercial search engines are now playing an increasingly important role in Web information dissemination and access. Of particular interest to business and national governments is whether the big engines have coverage biased towards the US or other countries. In our study we tested for national biases in three major search engines and found significant differences in their coverage of commercial Web sites. The US sites were much better covered than the others in the study: sites from China, Taiwan and Singapore. We then examined the possible technical causes of the differences and found that the language of a site does not affect its coverage by search engines. However, the visibility of a site, measured by the number of links to it, affects its chance to be covered by search engines. We conclude that the coverage bias does exist but this is due not to deliberate choices of the search engines but occurs as a natural result of cumulative advantage effects of US sites on the Web. Nevertheless, the bias remains a cause for international concern.

Date

14. 8.2004 10:30:29

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Abstract

Purpose Public attitudes towards COVID-19 and social distancing are critical in reducing its spread. It is therefore important to understand public reactions and information dissemination in all major forms, including on social media. This article investigates important issues reflected on Twitter in the early stages of the public reaction to COVID-19. Design/methodology/approach A thematic analysis of the most retweeted English-language tweets mentioning COVID-19 during March 10-29, 2020. Findings The main themes identified for the 87 qualifying tweets accounting for 14 million retweets were: lockdown life; attitude towards social restrictions; politics; safety messages; people with COVID-19; support for key workers; work; and COVID-19 facts/news. Research limitations/implications Twitter played many positive roles, mainly through unofficial tweets. Users shared social distancing information, helped build support for social distancing, criticised government responses, expressed support for key workers and helped each other cope with social isolation. A few popular tweets not supporting social distancing show that government messages sometimes failed. Practical implications Public health campaigns in future may consider encouraging grass roots social web activity to support campaign goals. At a methodological level, analysing retweet counts emphasised politics and ignored practical implementation issues. Originality/value This is the first qualitative analysis of general COVID-19-related retweeting.

Date

20. 1.2015 18:30:22