Search (34 results, page 1 of 2)

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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

Citation counts are widely used as indicators of research quality to support or replace human peer review and for lists of top cited papers, researchers, and institutions. Nevertheless, the relationship between citations and research quality is poorly evidenced. We report the first large-scale science-wide academic evaluation of the relationship between research quality and citations (field normalized citation counts), correlating them for 87,739 journal articles in 34 field-based UK Units of Assessment (UoA). The two correlate positively in all academic fields, from very weak (0.1) to strong (0.5), reflecting broadly linear relationships in all fields. We give the first evidence that the correlations are positive even across the arts and humanities. The patterns are similar for the field classification schemes of Scopus and Dimensions.ai, although varying for some individual subjects and therefore more uncertain for these. We also show for the first time that no field has a citation threshold beyond which all articles are excellent quality, so lists of top cited articles are not pure collections of excellence, and neither is any top citation percentile indicator. Thus, while appropriately field normalized citations associate positively with research quality in all fields, they never perfectly reflect it, even at high values.

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Abstract

Purpose The four major Subject Repositories (SRs), arXiv, Research Papers in Economics (RePEc), Social Science Research Network (SSRN) and PubMed Central (PMC), are all important within their disciplines but no previous study has systematically compared how often they are cited in academic publications. In response, the purpose of this paper is to report an analysis of citations to SRs from Scopus publications, 2000-2013. Design/methodology/approach Scopus searches were used to count the number of documents citing the four SRs in each year. A random sample of 384 documents citing the four SRs was then visited to investigate the nature of the citations. Findings Each SR was most cited within its own subject area but attracted substantial citations from other subject areas, suggesting that they are open to interdisciplinary uses. The proportion of documents citing each SR is continuing to increase rapidly, and the SRs all seem to attract substantial numbers of citations from more than one discipline. Research limitations/implications Scopus does not cover all publications, and most citations to documents found in the four SRs presumably cite the published version, when one exists, rather than the repository version. Practical implications SRs are continuing to grow and do not seem to be threatened by institutional repositories and so research managers should encourage their continued use within their core disciplines, including for research that aims at an audience in other disciplines. Originality/value This is the first simultaneous analysis of Scopus citations to the four most popular SRs.

Date

20. 1.2015 18:30:22

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Abstract

Purpose - The purpose of this research is to investigate general patterns of tag usage and determines the usefulness of the tags used within university image groups to the wider Flickr community. There has been a significant rise in the use of Web 2.0 social network web sites and online applications in recent years. One of the most popular is Flickr, an online image management application. Design/methodology/approach - This study uses a webometric data collection, classification and informetric analysis. Findings - The results show that members of university image groups tend to tag in a manner that is of use to users of the system as a whole rather than merely for the tag creator. Originality/value - This paper gives a valuable insight into the tagging practices of image groups in Flickr.

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Abstract

Collaboration is a major research policy objective, but does it deliver higher quality research? This study uses citation analysis to examine the Web of Science (WoS) Information Science & Library Science subject category (IS&LS) to ascertain whether, in general, more highly cited articles are more highly collaborative than other articles. It consists of two investigations. The first investigation is a longitudinal comparison of the degree and proportion of collaboration in five strata of citation; it found that collaboration in the highest four citation strata (all in the most highly cited 22%) increased in unison over time, whereas collaboration in the lowest citation strata (un-cited articles) remained low and stable. Given that over 40% of the articles were un-cited, it seems important to take into account the differences found between un-cited articles and relatively highly cited articles when investigating collaboration in IS&LS. The second investigation compares collaboration for 35 influential information scientists; it found that their more highly cited articles on average were not more highly collaborative than their less highly cited articles. In summary, although collaborative research is conducive to high citation in general, collaboration has apparently not tended to be essential to the success of current and former elite information scientists.

Date

22. 3.2009 12:43:51

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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

The web contains a huge number of digital pictures. For scholars publishing such images it is important to know how well used their images are, but no method seems to have been developed for monitoring the value of academic images. In particular, can the impact of scientific or artistic images be assessed through identifying images copied or reused on the Internet? This article explores a case study of 260 NASA images to investigate whether the TinEye search engine could theoretically help to provide this information. The results show that the selected pictures had a median of 11 online copies each. However, a classification of 210 of these copies reveals that only 1.4% were explicitly used in academic publications, reflecting research impact, and the majority of the NASA pictures were used for informal scholarly (or educational) communication (37%). Additional analyses of world famous paintings and scientific images about pathology and molecular structures suggest that image contents are important for the type and extent of image use. Although it is reasonable to use statistics derived from TinEye for assessing image reuse value, the extent of its image indexing is not known.

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Abstract

Blogs that cite academic articles have emerged as a potential source of alternative impact metrics for the visibility of the blogged articles. Nevertheless, to evaluate more fully the value of blog citations, it is necessary to investigate whether research blogs focus on particular types of articles or give new perspectives on scientific discourse. Therefore, we studied the characteristics of peer-reviewed references in blogs and the typical content of blog posts to gain insight into bloggers' motivations. The sample consisted of 391 blog posts from 2010 to 2012 in Researchblogging.org's health category. The bloggers mostly cited recent research articles or reviews from top multidisciplinary and general medical journals. Using content analysis methods, we created a general classification scheme for blog post content with 10 major topic categories, each with several subcategories. The results suggest that health research bloggers rarely self-cite and that the vast majority of their blog posts (90%) include a general discussion of the issue covered in the article, with more than one quarter providing health-related advice based on the article(s) covered. These factors suggest a genuine attempt to engage with a wider, nonacademic audience. Nevertheless, almost 30% of the posts included some criticism of the issues being discussed.

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Abstract

Interdisciplinary collaboration is a major goal in research policy. This study uses citation analysis to examine diverse subjects in the Web of Science and Scopus to ascertain whether, in general, research published in journals classified in more than one subject is more highly cited than research published in journals classified in a single subject. For each subject, the study divides the journals into two disjoint sets called Multi and Mono. Multi consists of all journals in the subject and at least one other subject whereas Mono consists of all journals in the subject and in no other subject. The main findings are: (a) For social science subject categories in both the Web of Science and Scopus, the average citation levels of articles in Mono and Multi are very similar; and (b) for Scopus subject categories within life sciences, health sciences, and physical sciences, the average citation level of Mono articles is roughly twice that of Multi articles. Hence, one cannot assume that in general, multidisciplinary research will be more highly cited, and the converse is probably true for many areas of science. A policy implication is that, at least in the sciences, multidisciplinary researchers should not be evaluated by citations on the same basis as monodisciplinary researchers.

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Abstract

Increasing interdisciplinarity has been a policy objective since the 1990s, promoted by many governments and funding agencies, but the question is: How deeply has this affected the social sciences? Although numerous articles have suggested that research has become more interdisciplinary, yet no study has compared the extent to which the interdisciplinarity of different social science subjects has changed. To address this gap, changes in the level of interdisciplinarity since 1980 are investigated for subjects with many articles in the Social Sciences Citation Index (SSCI), using the percentage of cross-disciplinary citing documents (PCDCD) to evaluate interdisciplinarity. For the 14 SSCI subjects investigated, the median level of interdisciplinarity, as measured using cross-disciplinary citations, declined from 1980 to 1990, but rose sharply between 1990 and 2000, confirming previous research. This increase was not fully matched by an increase in the percentage of articles that were assigned to more than one subject category. Nevertheless, although on average the social sciences have recently become more interdisciplinary, the extent of this change varies substantially from subject to subject. The SSCI subject with the largest increase in interdisciplinarity between 1990 and 2000 was Information Science & Library Science (IS&LS) but there is evidence that the level of interdisciplinarity of IS&LS increased less quickly during the first decade of this century.

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Abstract

The value of low frequency words for subject-based academic Web site clustering is assessed. A new technique is introduced to compare the relative clustering power of different vocabularies. The technique is designed for word frequency tests in large document clustering exercises. Results for the Australian and New Zealand academic Web spaces indicate that low frequency words are useful for clustering academic Web sites along subject lines; removing low frequency words results in sites becoming, an average, less dissimilar to sites from other subjects.

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Abstract

Journal-based citations are an important source of data for impact indices. However, the impact of journal articles extends beyond formal scholarly discourse. Measuring online scholarly impact calls for new indices, complementary to the older ones. This article examines a possible alternative metric source, blog posts aggregated at ResearchBlogging.org, which discuss peer-reviewed articles and provide full bibliographic references. Articles reviewed in these blogs therefore receive "blog citations." We hypothesized that articles receiving blog citations close to their publication time receive more journal citations later than the articles in the same journal published in the same year that did not receive such blog citations. Statistically significant evidence for articles published in 2009 and 2010 support this hypothesis for seven of 12 journals (58%) in 2009 and 13 of 19 journals (68%) in 2010. We suggest, based on these results, that blog citations can be used as an alternative metric source.

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Abstract

Finding new ways to help researchers and administrators understand academic fields is an important task for information scientists. Given the importance of interdisciplinary research, it is essential to be aware of disciplinary differences in aspects of scholarship, such as the significance of recent changes in a field. This paper identifies potential changes in 25 subject categories through a term comparison of words in article titles, keywords and abstracts in 1 year compared to the previous 4 years. The scholarly influence of new research issues is indirectly assessed with a citation analysis of articles matching each trending term. While topic-related words dominate the top terms, style, national focus, and language changes are also evident. Thus, as reflected in Scopus, fields evolve along multiple dimensions. Moreover, while articles exploiting new issues are usually more cited in some fields, such as Organic Chemistry, they are usually less cited in others, including History. The possible causes of new issues being less cited include externally driven temporary factors, such as disease outbreaks, and internally driven temporary decisions, such as a deliberate emphasis on a single topic (e.g., through a journal special issue).

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Abstract

General sentiment analysis for the social web has become increasingly useful for shedding light on the role of emotion in online communication and offline events in both academic research and data journalism. Nevertheless, existing general-purpose social web sentiment analysis algorithms may not be optimal for texts focussed around specific topics. This article introduces 2 new methods, mood setting and lexicon extension, to improve the accuracy of topic-specific lexical sentiment strength detection for the social web. Mood setting allows the topic mood to determine the default polarity for ostensibly neutral expressive text. Topic-specific lexicon extension involves adding topic-specific words to the default general sentiment lexicon. Experiments with 8 data sets show that both methods can improve sentiment analysis performance in corpora and are recommended when the topic focus is tightest.

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Abstract

Conducting a literature review is an important activity for postgraduates and many undergraduates. Librarians can play an important role, directing students to digital libraries, compiling online subject reSource lists, and educating about the need to evaluate the quality of online resources. In order to conduct an effective literature search in a new area, however, in some subjects it is necessary to gain basic topic knowledge, including specialist vocabularies. Google's link-based page ranking algorithm makes this search engine an ideal tool for finding specialist topic introductory material, particularly in computer science, and so librarians should be teaching this as part of a strategic literature review approach.

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Abstract

In this article Web issue analysis is introduced as a new technique to investigate an issue as reflected on the Web. The issue chosen, integrated water resource management (IWRM), is a United Nations-initiated paradigm for managing water resources in an international context, particularly in developing nations. As with many international governmental initiatives, there is a considerable body of online information about it: 41.381 hypertext markup language (HTML) pages and 28.735 PDF documents mentioning the issue were downloaded. A page uniform resource locator (URL) and link analysis revealed the international and sectoral spread of IWRM. A noun and noun phrase occurrence analysis was used to identify the issues most commonly discussed, revealing some unexpected topics such as private sector and economic growth. Although the complexity of the methods required to produce meaningful statistics from the data is disadvantageous to easy interpretation, it was still possible to produce data that could be subject to a reasonably intuitive interpretation. Hence Web issue analysis is claimed to be a useful new technique for information science.

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Abstract

Although citation counts are often used to evaluate the research impact of academic publications, they are problematic for books that aim for educational or cultural impact. To fill this gap, this article assesses whether a number of simple metrics derived from Amazon.com reviews of academic books could provide evidence of their impact. Based on a set of 2,739 academic monographs from 2008 and a set of 1,305 best-selling books in 15 Amazon.com academic subject categories, the existence of significant but low or moderate correlations between citations and numbers of reviews, combined with other evidence, suggests that online book reviews tend to reflect the wider popularity of a book rather than its academic impact, although there are substantial disciplinary differences. Metrics based on online reviews are therefore recommended for the evaluation of books that aim at a wide audience inside or outside academia when it is important to capture the broader impacts of educational or cultural activities and when they cannot be manipulated in advance of the evaluation.

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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.