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  • × author_ss:"Thelwall, M."
  1. Thelwall, M.; Buckley, K.; Paltoglou, G.; Cai, D.; Kappas, A.: Sentiment strength detection in short informal text (2010) 0.01 
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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
  2. Thelwall, M.; Ruschenburg, T.: Grundlagen und Forschungsfelder der Webometrie (2006) 0.00 
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    Date
    4.12.2006 12:12:22
  3. Levitt, J.M.; Thelwall, M.: Citation levels and collaboration within library and information science (2009) 0.00 
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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
  4. Thelwall, M.; Buckley, K.; Paltoglou, G.: Sentiment in Twitter events (2011) 0.00 
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    Date
    22. 1.2011 14:27:06
  5. Thelwall, M.; Maflahi, N.: Guideline references and academic citations as evidence of the clinical value of health research (2016) 0.00 
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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) 0.00 
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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) 0.00 
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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) 0.00 
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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) 0.00 
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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) 0.00 
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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) 0.00 
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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) 0.00 
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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) 0.00 
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    Date
    22. 6.2023 18:11:50
  14. Thelwall, M.; Foster, D.: Male or female gender-polarized YouTube videos are less viewed (2021) 0.00 
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    Abstract
    As one of the world's most visited websites, YouTube is potentially influential for learning gendered attitudes. Nevertheless, despite evidence of gender influences within the site for some topics, the extent to which YouTube reflects or promotes male/female or other gender divides is unknown. This article analyses 10,211 YouTube videos published in 12 months from 2014 to 2015 using commenter-portrayed genders (inferred from usernames) and view counts from the end of 2019. Nonbinary genders are omitted for methodological reasons. Although there were highly male and female topics or themes (e.g., vehicles or beauty) and male or female gendering is the norm, videos with topics attracting both males and females tended to have more viewers (after approximately 5 years) than videos in male or female gendered topics. Similarly, within each topic, videos with gender balanced sets of commenters tend to attract more viewers. Thus, YouTube does not seem to be driving male-female gender differences.
  15. Thelwall, M.; Buckley, K.; Paltoglou, G.: Sentiment strength detection for the social web (2012) 0.00 
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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.
  16. Thelwall, M.: Web indicators for research evaluation : a practical guide (2016) 0.00 
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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.

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