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  • × author_ss:"Thelwall, M."
  1. Thelwall, M.; Buckley, K.; Paltoglou, G.: Sentiment in Twitter events (2011) 0.03 
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    Abstract
    The microblogging site Twitter generates a constant stream of communication, some of which concerns events of general interest. An analysis of Twitter may, therefore, give insights into why particular events resonate with the population. This article reports a study of a month of English Twitter posts, assessing whether popular events are typically associated with increases in sentiment strength, as seems intuitively likely. Using the top 30 events, determined by a measure of relative increase in (general) term usage, the results give strong evidence that popular events are normally associated with increases in negative sentiment strength and some evidence that peaks of interest in events have stronger positive sentiment than the time before the peak. It seems that many positive events, such as the Oscars, are capable of generating increased negative sentiment in reaction to them. Nevertheless, the surprisingly small average change in sentiment associated with popular events (typically 1% and only 6% for Tiger Woods' confessions) is consistent with events affording posters opportunities to satisfy pre-existing personal goals more often than eliciting instinctive reactions.
    Date
    22. 1.2011 14:27:06
  2. Thelwall, M.; Buckley, K.; Paltoglou, G.; Cai, D.; Kappas, A.: Sentiment strength detection in short informal text (2010) 0.02 
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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
  3. Thelwall, M.; Ruschenburg, T.: Grundlagen und Forschungsfelder der Webometrie (2006) 0.01 
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    Date
    4.12.2006 12:12:22
  4. 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
  5. 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
  6. 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
  7. 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
  8. Kousha, K.; Thelwall, M.: How is science cited on the Web? : a classification of google unique Web citations (2007) 0.01 
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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.
  9. 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
  10. 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
  11. Thelwall, M.: Are Mendeley reader counts high enough for research evaluations when articles are published? (2017) 0.01 
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    Date
    20. 1.2015 18:30:22
  12. 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
  13. 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
  14. Thelwall, M.: ¬A comparison of sources of links for academic Web impact factor calculations (2002) 0.00 
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    Source
    Journal of documentation. 58(2002) no.1, S.66-78
  15. Thelwall, M.: Interpreting social science link analysis research : a theoretical framework (2006) 0.00 
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    Source
    Journal of the American Society for Information Science and Technology. 57(2006) no.1, S.60-68
  16. Thelwall, M.; Vaughan, L.: New versions of PageRank employing alternative Web document models (2004) 0.00 
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    Source
    Aslib proceedings. 56(2004) no.1, S.24-33
  17. Thelwall, M.: Extracting accurate and complete results from search engines : case study windows live (2008) 0.00 
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    Source
    Journal of the American Society for Information Science and Technology. 59(2008) no.1, S.38-50
  18. Angus, E.; Thelwall, M.; Stuart, D.: General patterns of tag usage among university groups in Flickr (2008) 0.00 
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    Source
    Online information review. 32(2008) no.1, S.89-101
  19. Thelwall, M.; Wilkinson, D.; Uppal, S.: Data mining emotion in social network communication : gender differences in MySpace (2009) 0.00 
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    Journal of the American Society for Information Science and Technology. 61(2010) no.1, S.190-199
  20. Shema, H.; Bar-Ilan, J.; Thelwall, M.: Do blog citations correlate with a higher number of future citations? : Research blogs as a potential source for alternative metrics (2014) 0.00 
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    Date
    1. 5.2014 18:06:01

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