Search (27 results, page 1 of 2)

Thelwall, M.; Buckley, K.; Paltoglou, G.: Sentiment in Twitter events (2011) 0.03

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Date: 22. 1.2011 14:27:06
Source: Journal of the American Society for Information Science and Technology. 62(2011) no.2, S.406-418

Kousha, K.; Thelwall, M.: How is science cited on the Web? : a classification of google unique Web citations (2007) 0.03
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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.

Thelwall, M.; Buckley, K.; Paltoglou, G.; Cai, D.; Kappas, A.: Sentiment strength detection in short informal text (2010) 0.03

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Date: 22. 1.2011 14:29:23
Footnote: Vgl. auch das Erratum in: Journal of the American Society for Information Science and Technology. 62(2011) no.2, S.419

Thelwall, M.: Are Mendeley reader counts high enough for research evaluations when articles are published? (2017) 0.03

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Date: 20. 1.2015 18:30:22
Source: Aslib journal of information management. 69(2017) no.2, S.174-183

Thelwall, M.; Ruschenburg, T.: Grundlagen und Forschungsfelder der Webometrie (2006) 0.01

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Date: 4.12.2006 12:12:22

Levitt, J.M.; Thelwall, M.: Citation levels and collaboration within library and information science (2009) 0.01
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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

Thelwall, M.; Maflahi, N.: Guideline references and academic citations as evidence of the clinical value of health research (2016) 0.01

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Date: 19. 3.2016 12:22:00

Thelwall, M.; Sud, P.: Mendeley readership counts : an investigation of temporal and disciplinary differences (2016) 0.01

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Date: 16.11.2016 11:07:22

Didegah, F.; Thelwall, M.: Co-saved, co-tweeted, and co-cited networks (2018) 0.01

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Date: 28. 7.2018 10:00:22

Thelwall, M.; Sud, P.; Wilkinson, D.: Link and co-inlink network diagrams with URL citations or title mentions (2012) 0.01

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Date: 6. 4.2012 18:16:22

Li, X.; Thelwall, M.; Kousha, K.: ¬The role of arXiv, RePEc, SSRN and PMC in formal scholarly communication (2015) 0.01

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Date: 20. 1.2015 18:30:22

Thelwall, M.; Thelwall, S.: ¬A thematic analysis of highly retweeted early COVID-19 tweets : consensus, information, dissent and lockdown life (2020) 0.01

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Date: 20. 1.2015 18:30:22

Thelwall, M.; Kousha, K.; Abdoli, M.; Stuart, E.; Makita, M.; Wilson, P.; Levitt, J.: Why are coauthored academic articles more cited : higher quality or larger audience? (2023) 0.01

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Date: 22. 6.2023 18:11:50

Thelwall, M.: Web impact factors and search engine coverage (2000) 0.01

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Source: Journal of documentation. 56(2000) no.2, S.185-189

Vaughan, L.; Thelwall, M.: ¬A modelling approach to uncover hyperlink patterns : the case of Canadian universities (2005) 0.01

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Source: Information processing and management. 41(2005) no.2, S.347-360

Thelwall, M.: Can Google's PageRank be used to find the most important academic Web pages? (2003) 0.01

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Source: Journal of documentation. 59(2003) no.2, S.205-217

Thelwall, M.: Homophily in MySpace (2009) 0.01

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Source: Journal of the American Society for Information Science and Technology. 60(2009) no.2, S.219-231

Shifman, L.; Thelwall, M.: Assessing global diffusion with Web memetics : the spread and evolution of a popular joke (2009) 0.01

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Date: 2. 2.2010 19:49:33

Thelwall, M.; Wilkinson, D.: Public dialogs in social network sites : What is their purpose? (2010) 0.01

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Source: Journal of the American Society for Information Science and Technology. 61(2010) no.2, S.392-404

Thelwall, M.; Buckley, K.: Topic-based sentiment analysis for the social web : the role of mood and issue-related words (2013) 0.01
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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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