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Professor Michael Thelwall

Professor Michael Thelwall

Professor of Data Science

  • Email address
  • Phone number 01902 321470
  • Faculty Faculty of Science and Engineering
  • Institute School of Mathematics and Computer Science
  • Areas of expertise

    Maths, statistics, research evaluation, webometrics, scientometrics, altmetrics


Mike Thelwall is Professor of Data Science and leader of the Statistical Cybermetrics Research Group at the University of Wolverhampton, which he joined in 1989. He is also Docent at the Department of Information Studies at Åbo Akademi University, and a Professor at the University of Malaya. His PhD was in Pure Mathematics from the University of Lancaster. His current research field includes identifying and analysing web phenomena using quantitative-led research methods, including altmetrics and sentiment analysis, and has pioneered an information science approach to link analysis. Mike has developed a wide range of tools for gathering and analysing web data, including hyperlink analysis, sentiment analysis and content analysis for Twitter, YouTube, MySpace, blogs and the web in general. His 400+ publications include 355 refereed journal articles and three books, including Introduction to Webometrics. He is an associate editor of the Journal of the Association for Information Science and Technology and sits on four other editorial boards. For more information, see:

  • Science measurement
  • Impact measurement
  • Sentiment analysis
  • Social media analysis

BSc, PhD

Altmetrics, Social Media Indicators and Webometrics (Kousha metrics)

  1. [Mendeley] Thelwall, M. (in press). Mendeley reader counts for US computer science conference papers and journal articles. Quantitative Science Studies. [Mendeley reader counts have a substantial advantage over citation counts as ain impact indicator for recently-published conference papers due to their greater speed.]
  2. [News] Kousha, K. & Thelwall, M. (2019). An automatic method to identify citations to journals in news stories: A case study of the UK newspapers citing Web of Science journals, Journal of Data and Information Science, 4(3), 73-95. [Introduces a program to process ProQuest news articles and find citations to journals; assesses which journals are cited by the UK press.]
  3. [Mendeley] Kousha, K. & Thelwall, M. (2019). Can Google Scholar and Mendeley help to assess the scholarly impacts of dissertations? Journal of Informetrics, 13(3), 467-484. [publisher version] [Google Scholar queries and Mendeley reader count can be used to systematically identify citation data for large numbers of dissertations for research evaluation purposes.]
  4. [Syllabi] Mas Bleda, A. & Thelwall, M. (2018). Assessing the teaching value of non-English academic books: The case of Spain. Revista Española de Documentación Científica, 41(4), e222. [Online syllabus mentions can help to assess the teaching value of Spanish-language books, but manual checks are necessary if assessing individual books.]
  5. [Mendeley] Thelwall, M. (2018). Does female-authored research have more educational impact than male-authored research? Journal of Altmetrics. 1(1), p.3. [local copy][Female first-authored articles attract more student Mendeley readers than male-authored articles in Spain, Turkey, the UK and USA but not India.] [Nature Career brief about this][THES article] [Nature Index article]
  6. [All] Thelwall, M. (2018). Altmetric prevalence in the social sciences, arts and humanities: Where are the online discussions? Journal of Altmetrics, 1(1), p.4. [There is surprisingly little social web discussion about arts and humanities journal articles.]
  7. [ResearchGate] Lepori, B., Thelwall, M. & Hoorani, B. H. (2018). Which US and European Higher Education Institutions are visible in ResearchGate and what affects their RG Score? Journal of Informetrics, 12(3), 806-818. [publisher version] [Institutional ResearchGate scores reflect research volume rather than research visibility.]
  8. [Twitter] Mohammadi, E., Thelwall, M., Kwasny, M., & Holmes, K. (2018). Academic information on Twitter: A user survey. PLOS ONE, 13(5): e0197265. [A survey of people that tweet about academic research. A surprisingly high proportion are not academics.]
  9. [Mendeley]Thelwall, M. (2018). Early Mendeley readers correlate with later citation counts. Scientometrics, 115(3), 1231–1240. [publisher full text view only] [Mendeley reader counts within a month of publication correlate significantly with citation counts 20 months later in 10 fields, so it is reasonable to use early reader counts as evidence of likely long term citation impact.]
  10. [Twitter, Mendeley] Didegah, F. & Thelwall, M. (2018). Co-saved, co-tweeted and co-cited networks. Journal of the Association for Information Science and Technology, 69(8), 959-973.[There is very little overlap between co-saved, co-tweeted and co-cited networks.]
  11. [All] Thelwall, M. & Nevill, T. (2018). Could scientists use scores to predict longer term citation counts? [free access] [w] [data] Journal of Informetrics, 12(1), 237–248. [ scores can be used to help predict future citation counts, especially if the Mendeley reader component is included. Considering both scores and journal impact factors gives the best predictions. scores also seem to partly reflect non-scholarly impact dimensions in some fields.]
  12. [Mendeley] Thelwall, M. (2018). Differences between journals and years in the proportions of students, researchers and faculty registering Mendeley articles. [read-only publisher version] Scientometrics, 115(2), 717-729. doi:10.1007/s11192-018-2689-7 [There are substantial differences between journals in the proportions of different types of reader in Mendeley, so Mendeley reader counts for different journals and fields represent different reader demographics. There are also substantial differences between journals in similar areas for the rate at which Mendeley readers accumulate.]
  13. [Mendeley] Thelwall, M. (2017). Are Mendeley reader counts useful impact indicators in all fields? Scientometrics, 113(3), 1721–1731. doi:10.1007/s11192-017-2557-x [Correlations between Mendeley reader counts and Scopus citation counts are strong in almost all of 325 narrow Scopus fields checked, so Mendeley reader counts are an almost universally strong citation impact indicator.]
  14. [Mendeley] Thelwall, M. (2019).  Do Mendeley reader counts indicate the value of arts and humanities research?  Journal of Librarianship & Information Science, 51(3), 781-788. [Mendeley readership counts reflect Scopus citation counts in the arts and humanities as strongly as in other areas of scholarship.]
  15. [Mendeley] Thelwall, M. (2017). Are Mendeley reader counts high enough for research evaluations when articles are published? Aslib Journal of Information Management, 69(2), 174-183. doi:10.1108/AJIM-01-2017-0028 [Articles in 10 disciplines attracted 0.1 to 0.8 Mendeley readers per article in the month in which they first appeared in Scopus. This is about ten times more than the average Scopus citation count.]
  16. [Mendeley] Maflahi, N, & Thelwall, M. (2018). How quickly do publications get read? The evolution of Mendeley reader counts for new articles. Journal of the Association for Information Science and Technology,69(1), 158–167. doi:10.1002/asi.23909 [Articles may have substantial numbers of readers by their publication date, making reader counts useful for immediate impact assessment. This depends on the length of the journal's publication backlog.]
  17. [ResearchGate] Thelwall, M., & Kousha, K. (2017). ResearchGate versus Google Scholar: Which finds more early citations? Scientometrics, 112(2), 1125-1131. doi:10.1007/s11192-017-2400-4 [ResearchGate index more early citations than Scopus and the Web of Science but less than Google Scholar.]
  18. [ResearchGate] Orduna-Malea, E., Martín-Martín, A., Thelwall, M., & Delgado López-Cózar, E. (2017). Do ResearchGate Scores create ghost academic reputations? Scientometrics. 112(1), 443-460. doi:10.1007/s11192-017-2396-9. See LSE Impact Blog post.
  19. [Mendeley] Aduku, J., Thelwall, M. & Kousha, K. (2017). Do Mendeley reader counts reflect the scholarly impact of conference papers? An investigation of Computer Science and Engineering. Scientometrics, 112(1), 573-581. doi:10.1007/s11192-017-2367-1 [Mendeley reader counts are more useful for computing types of engineering than for building and manufacturing engineering, in terms of conference papers.]
  20. [Drug guidelines] Thelwall, M., Kousha, K. & Abdoli, M. (2017). Is medical research informing professional practice more highly cited? Evidence from AHFS DI Essentials in Scientometrics, 112(1), 509-527. doi:10.1007/s11192-017-2292-3 [It is possible to systematically harvest citations to academic research from the drug guidelines. Papers in guidelines reference lists tend to be more highly cited than average for the publishing journal.]
  21. [Mendeley] Thelwall, M. (2017). Does Mendeley provide evidence of the educational value of journal articles? Learned Publishing, 30(2), 107-113. doi:10.1002/leap.1076. [Mendeley student reader counts are broadly in line with Mendeley researcher reader counts for most subject areas except maths, where undergraduates avoid most papers.]
  22. [All] Thelwall, M. (2017). Three practical field normalised alternative indicator formulae for research evaluation.  Journal of Informetrics, 11(1), 128–151. 10.1016/j.joi.2016.12.002 [A robust indicator is introduced for citation counts that allows narrower confidence intervals to be calculated for more powerful analyses. Two new proportion cited indicators are introduced to allow more powerful web indicators when a low proportion of outputs have a non-zero indicator score.] [EMNPC worked examplesMNLCS worked examples]
  23. [Patent citations] Orduna-Malea, E., Thelwall, M. & Kousha, K. (2017). Web citations in patents: Evidence of technological impact? Journal of the Association for Information Science and Technology, 68(8), 1967-1974. doi:10.1002/asi.23821 [URL citations in online patents are common enough to be used to help rank major US universities for an aspect of technological impact.]
  24. [All] Thelwall, M. (2017). Web indicators for research evaluation: A practical guide. San Rafael, CA: Morgan & Claypool. [Book: Gives an overview of all the steps needed from data collection to analysis and interpretation for web indicators, including practical advice.]
  25. [SlideShare] Thelwall, M. & Kousha, K. (2017). SlideShare presentations, citations, users and trends: A professional site with academic and educational uses. Journal of the Association for Information Science and Technology, 68(8), 1989-2003. [SlideShare is a presentation-centred site with a predominantly professional user base that is a useful source of non-academic information for scholars and students.]
  26. [Patent citations, online presentation mentions, online course syllabus mentions, Wikipedia mentions, Mendeley reader counts, data] Mas-Bleda, A. & Thelwall, M. (2016). Can alternative indicators overcome language biases in citation counts? A comparison of Spanish and UK research. Scientometrics, 109(3), 2007-2030. doi:10.1007/s11192-016-2118-8 [General web and social web indicators increase the apparent bias of indicators against Spanish research in comparison to the UK, probably due to lower social web uptake in Spain.]
  27. [Clinical trials] Thelwall, M. & Kousha, K. (2016). Are citations from clinical trials evidence of higher impact research? An analysis of Scientometrics, 109(2), 1341-1351. doi:10.1007/s11192-016-2112-1 [Citations can be gathered from online clinical trials records and used as a health impact indicator.]
  28. [All] Thelwall, M. (2016). Interpreting correlations between citation counts and other indicators. Scientometrics, 108(1), 337-347. doi:10.1007/s11192-016-1973-7 [The magnitude of correlations between citation counts and other indicators depends on the average citation counts and average indicator values as well as the underlying association between them, so correlations for different data sets are often not comparable.R code and results. [Read-only publisher version]
  29. [All] Thelwall, M. (2016). Data science altmetrics. Journal of Data and Information Science, 1(2), 7-12. doi:10.20309/jdis.201610. [Discusses altmetrics from a big data perspective.]
  30. [Google Code software] Thelwall, M. & Kousha, K. (2016). Academic software downloads from Google Code: Useful usage indicators? Information Research. 21(1), paper 709.  [Download counts can be a useful indicator of the wider uptake of academic software.]
  31. [Figshare] Thelwall, M. & Kousha, K. (2016). Figshare: A universal repository for academic resource sharing? Online Information Review, 40(3), 333-346. doi:10.1108/OIR-06-2015-0190 [The repository FigShare host resources from some subject areas more than others but the uptake of its resources does not depend on their subject area.]
  32. [Wikipedia] Kousha, K. & Thelwall, M. (2017). Are Wikipedia citations important evidence of the impact of scholarly articles and books? Journal of the Association for Information Science and Technology, 68(3), 762-779. doi:10.1002/asi.23694 [Citations from Wikipedia are frequent enough to be useful impact indicators for books in the humanities.]
  33. [ResearchGate] Thelwall, M., & Kousha, K. (2017). ResearchGate articles: Age, discipline, audience size and impact. Journal of the Association for Information Science and Technology, 68(2), 468-479. doi:10.1002asi.23675 [Article views in ResearchGate have a significant positive correlation with Scopus citations but seem to reflect a wider audience than scholarly citations.]
  34. [altmetrics and webometrics] Thelwall, M., Kousha, K., Dinsmore, A. & Dolby, K. (2016). Alternative metric indicators for funding scheme evaluations. Aslib Journal of Information Management, 68(1), 2-18. doi:10.1108/AJIM-09-2015-0146 [Some alternative indicators can aid funding agencies’ evaluations of their funding schemes, if used carefully.]
  35. [Web citations and links] Thelwall, M., & Kousha, K. (2015). Web indicators for research evaluation, part 1: Citations and links to academic articles from the web. El Profesional de la Información, 24(5), 587-606. doi:10.3145/epi.2015.sep.08 [Reviews research into generating academic indicators from the web.]
  36. [All social media] Thelwall, M., & Kousha, K. (2015). Web indicators for research evaluation, part 2: Social media metrics. El Profesional de la Información, 24(5), 607-620. doi:10.3145/epi.2015.sep.09 [Reviews research into generating academic indicators from social media.]
  37. [All social media] Kousha, K. & Thelwall, M. (2015). Web indicators for research evaluation, part 3: Books and non-standard outputs. El Profesional de la Información, 24(6), 724-736. doi:10.3145/epi.2015.nov.04 [Reviews research into generating academic indicators for books and other non-standard research outputs from the web.]
  38. [Mendeley] Fairclough, R. & Thelwall, M. (2015). National research impact indicators from Mendeley readers. Journal of Informetrics, 9(4), 845–859. doi:10.1016/j.joi.2015.08.003. [Mendeley reader counts can be used instead of citations for national research impact indicators and seem to identify trends about a year earlier.]
  39. [Mendeley] Thelwall, M. (2017). Why do papers have many Mendeley readers but few Scopus-indexed citations and vice versa? Journal of Librarianship & Information Science, 49(2), 144-151. doi:10.1177/0961000615594867 [A list of reasons why articles can have many Mendeley readers but few Scopus-indexed citations and vice versa.]
  40. [All] Thelwall, M. & Delgado, M. (2015). Arts and humanities research evaluation: No metrics please, just data. Journal of Documentation, 71(4), 817-833. DOI:10.1108/JD-02-2015-0028 [Arts and humanities researchers should be encouraged to think creatively about the kinds of data that they may be able to generate in support of the value of their research and should not rely upon standardised metrics.]
  41. [Mendeley] Thelwall, M. & Sud, P. (2016). Mendeley readership counts: An investigation of temporal and disciplinary differences. Journal of the Association for Information Science and Technology, 57(6), 3036-3050. doi:10.1002/asi.23559 [Mendeley reader counts increase more quickly than do citation counts across many different areas of research and stabilise after about five years. Coupled with high correlations between Mendeley readers and citations, this confirms the value of Mendeley reader counts as early evidence of impact for research.]
  42. [Mendeley] Thelwall, M. & Wilson, P. (2016). Mendeley readership altmetrics for medical articles: An analysis of 45 fields, Journal of the Association for Information Science and Technology, 67(8), 1962-1972. doi:10.1002/asi.23501 [Using the new Mendeley API with its more comprehensive information, shows that Mendeley bookmarks correlate highly (0.7) with citations to medical articles from 2009 in almost all fields and that readership counts follow a lognormal or a hooked power law distribution rather than a power law.]
  43. [Mendeley] Mohammadi, E., Thelwall, M. & Kousha, K. (2016). Can Mendeley bookmarks reflect readership? A survey of user motivations. Journal of the Association for Information Science and Technology, 67(5), 1198-1209. doi:10.1002/asi.23477 [Based on a survey of Mendeley users, articles are bookmarked in Mendeley mainly because they have been read or intend to be read. Hence Mendeley bookmarks can be used as indicators of readership for articles, at least for Mendeley users.]
  44. [Mendeley] Thelwall, M. & Maflahi, N. (2016). Guideline references and academic citations as evidence of the clinical value of health research. Journal of the Association for Information Science and Technology, 67(4), 960-966. doi:10.1002/asi.23432. [Articles cited in UK Clinical Knowledge Summaries are more highly cited and more highly read in Mendeley than comparable articles and so such articles make a contribution to both knowledge and practice.]
  45. [Amazon book reviews, Google Books] Kousha, K. & Thelwall, M. (2016). Can reviews help to assess the wider impacts of books? Journal of the Association for Information Science and Technology, 67(3), 566-581. doi:10.1002/asi.23404.[Introduces Amazon book reviews (number and sentiment) as metrics for academic book impact. Shows that book reviews tend to reflect the wider popularity of books rather than their purely academic impact.]
  46. [Mendeley] Maflahi, N. & Thelwall, M. (2016). When are readership counts as useful as citation counts? Scopus vs. Mendeley for LIS journals. Journal of the Association for Information Science and Technology, 67(1), 191-199. [Based upon four key LIS journals, finds that Mendeley reader counts are reasonable proxies for citation counts for articles that are 1-10 years old.]
  47. [Google Scholar, Microsoft Academic Search, Mendeley, Academia, LinkedIn, SlideShare] Mas-Bleda, A., Thelwall, M., Kousha, K. & Aguillo, I.F. (2014). Do highly cited researchers successfully use the Social Web? Scientometrics, 101(1), 337-356. [Shows that few European highly cited researchers use social web sites.]
  48. [Mendeley] Mohammadi, E., Thelwall, M., Haustein, S., & Larivière, V. (2015). Who reads research articles? An altmetrics analysis of Mendeley user categories.  Journal of the Association for Information Science and Technology, 66(9), 1832-1846. doi:10.1002/asi.23286. [PhD students, postgraduates and postdocs are the main readers of articles in Mendeley, although there are disciplinary differences.]
  49. [Mendeley] Thelwall, M. & Maflahi, N. (2015). Are scholarly articles disproportionately read in their own country? An analysis of Mendeley readers. Journal of the American Society for Information Science and Technology, 66(6), 1124–1135. doi:10.1002/asi.23252 [Articles tend to be read more in a country if some of the authors are from that country.]
  50. [ResearchGate] Thelwall, M. & Kousha, K. (2015). ResearchGate: Disseminating, communicating and measuring scholarship? Journal of the American Society for Information Science and Technology 66(5). 876–889. doi:10.1002/asi.23236 [Statistics reported by ResearchGate about its users broadly reflect traditional academic hierarchies, at least at the country level, but some countries make much more use of ResearchGate than do others.].
  51. [Blogs] Shema, H., Bar-Ilan, J., & Thelwall, M. (2015). How is research blogged? A content analysis approach. Journal of the Association for Information Science and Technology, 66(6), 1136–1149. doi:10.1002/asi.23239 [Shows that health research bloggers tend to cover others's work, seem to aim at a general audience, and often include critical comments.]
  52. [Twitter] Holmberg, K. & Thelwall, M. (2014). Disciplinary differences in Twitter scholarly communication, Scientometrics, 101(2), 1027-1042. [Shows that the extent to which researchers use Twitter for conversations, information sharing and research-relevant topics varies by discipline.]
  53. [All altmetrics] Sud, P. & Thelwall, M. (2014). Evaluating altmetrics. Scientometrics, 98(2),1131-1143.[Summarises methods to evaluate altmetrics and recommends evaluation strategies.]
  54. [Twitter] Thelwall, M. Tsou, A., Weingart, S., Holmberg, K., & Haustein, S. (2013). Tweeting links to academic articles, Cybermetrics, 17(1).
  55. [Mendeley] Mohammadi, E. & Thelwall, M. (2014). Mendeley readership altmetrics for the social sciences and humanities: Research evaluation and knowledge flows. Journal of the American Society for Information Science and Technology 65(8), 1627-1638.[Shows that Mendeley readership data can give evidence of knowledge transfer between disciplines.]
  56. [Grey literature] Wilkinson, D., Sud, P., & Thelwall, M. (2014). Substance without citation: Evaluating the online impact of grey literature. Scientometrics, 98(2), 797-806. [publisher version]
  57. [Blogs] Shema, H., Bar-Ilan, J., & Thelwall, M. (2014). Do blog citations correlate with a higher number of future citations? Research blogs as a potential source for alternative metrics. Journal of the American Society for Information Science and Technology, 65(5), 1018–1027.[Shows that blog citations can be used to predict future scholarly citations.]
  58. [Twitter, Blogs, Facebook, Google+, forums, mainstream media, LinkedIn, Reddit, Pinterest, research highlights, Q&A] Thelwall, M., Haustein, S., Larivière, V. & Sugimoto, C. (2013). Do altmetrics work? Twitter and ten other candidates. PLOS ONE, 8(5): e64841. doi:10.1371/journal.pone.0064841
  59. [F1000] Mohammadi, E., & Thelwall, M.(2013). Assessing non-standard article impact using F1000 labels. Scientometrics, 97(2), 383-395.
  60. [Web] Eccles, K.E., Thelwall, M., & Meyer, E.T. (2012). Measuring the web impact of digitised scholarly resources. Journal of Documentation, 68(4), 512-526.
  61. [Blogs] Shema, H., Bar-Ilan, J., & Thelwall, M. (2012). Research blogs and the discussion of scholarly information. PLoS ONE, 7(5): e35869.
  62. [YouTube views] Kousha, K., Thelwall, & Abdoli, M. (2012). The role of online videos in research communication: A content analysis of YouTube videos cited in academic publications. Journal of the American Society for Information Science and Technology, 63(9), 1710–1727.
  63. [CiteULike, Mendeley] Li, X., Thelwall, M., & Giustini, D. (2012). Validating online reference managers for scholarly impact measurement, Scientometrics, 91(2), 461-471.
  64. [Any altmetric]Thelwall, M. (2012). Journal impact evaluation: A webometric perspective, Scientometrics, 92(2), 429-441.
  65. [F1000, Mendeley] Li, X., & Thelwall, M. (2012). F1000, Mendeley and traditional bibliometric indicators. 17th International Conference on Science and Technology Indicators (Vol. 3, pp. 1–11).
  66. [Image copies] Kousha, K. & Thelwall, M. & Rezaie, S. (2010). Can the impact of scholarly images be assessed online?  An exploratory study using image identification technology, Journal of the American Society for Information Science and Technology, 61(9), 1734–1744.
  67. [Google Scholar, Google Books, Google Blogs, PowerPoint, course reading lists] Kousha, K., Thelwall, M. & Rezaie, S. (2010). Using the Web for research evaluation: The Integrated Online Impact indicator, Journal of Informetrics, 4(1), 124-135.
  68. [Web syllabuses] Kousha, K. & Thelwall, M. (2008). Assessing the impact of disciplinary research on teaching:  An automatic analysis of online syllabuses, Journal of the American Society for Information Science and Technology, 59(13), 2060-2069.
  69. [PowerPoint] Thelwall, M. & Kousha, K. (2008). Online presentations as a source of scientific impact?: An analysis of PowerPoint files citing academic journals. Journal of the American Society for Information Science and Technology, 59(5), 805 – 815.
  70. [Web] Kousha, K. & Thelwall, M. (2007). The web impact of open access social science research. Library and Information Science Research, 29(4), 495-507
  71. [Google Scholar] Kousha, K. & Thelwall, M. (2008). Sources of Google Scholar citations outside the Science Citation Index: A comparison between four science disciplines. Scientometrics, 74(2), 273-294.
  72. [Web] Kousha, K. & Thelwall, M. (2007). How is science cited on the web? A classification of Google unique web citations. Journal of the American Society for Information Science and Technology, 58(11), 1631-1644.
  73. [Web citations, Web/URL citations] Kousha, K. & Thelwall, M. (2007). Google Scholar citations and Google Web/URL citations: A multi-discipline exploratory analysis, Journal of the American Society for Information Science and Technology, 57(6), 1055-1065.
  74. [Web citations, URL citations] Kousha, K. & Thelwall, M. (2006). Motivations for URL citations to open access LIS library and information science articles. Scientometrics , 68(3), 501-517.

Book Citation Analysis

  1. Kousha, K. & Thelwall, M. (2018). Can Microsoft Academic help to assess the citation impact of academic books? Journal of Informetrics,12(3), 972-984. [Automatic Microsoft Academic queries find more citations to books than the Web of Science Book Citation Index.]
  2. Kousha, K., Thelwall, M. & Abdoli, M. (2017). Goodreads reviews to assess the wider impacts of books. Journal of the Association for Information Science and Technology, 68(8), 2004-2016. [Most arts, humanities and social sciences scholarly books in Scopus have at least one Goodreads review - counting Goodreads reviews gives a new impact indicator.]Kousha, K. & Thelwall, M. (2016). An automatic method for assessing the teaching impact of books from online academic syllabi. Journal of the Association for Information Science and Technology, 67(12), 2993-3007. [Citations from online academic syllabi are a practical source of evidence for the educational impact of books.]
  3. Thelwall, M. & Sud, P. (2014). No citation advantage for monograph-based collaborations? Journal of Informetrics, 8(1), 276-283.[Solo monographs tend to be as highly cited as co-authored monographs, unlike the case of articles.]
  4. Kousha, K. & Thelwall, M. (2015). An automatic method for extracting citations from Google Books. Journal of the American Society for Information Science and Technology, 66(2), 309–320. [Citations can be automatically extracted from Google Books and this is useful for social sciences and humanities research evaluation.]
  5. Abdullah, A. & Thelwall, M. (2014). Can the impact of non-Western academic books be measured? An investigation of Google Books and Google Scholar for Malaysia. Journal of the American Society for Information Science and Technology, 65(12), 2498–2508. 10.1002/asi.23145. [Citations from Google Books can be found for the monographs and edited volumes of at least one non-Western nation.]
  6. Kousha, K., Thelwall, & Rezaie, S. (2011). Assessing the citation impact of books: The role of Google Books, Google Scholar and Scopus. Journal of the American Society for Information Science and Technology, 62(11) 2147–2164.
  7. Kousha, K. & Thelwall, M. (2009). Google Book Search: Citation analysis for social science and the humanities, Journal of the American Society for Information Science and Technology, 60(8), 1537-1549.

Bibliometrics and Scientometrics

  1. Vera-Baceta, M., Thelwall, M. & Kousha, K. (in press). Web of Science and Scopus language coverage, Scientometrics. [Scopus and the Web of Science have different coverage of non-English languages, with each having strengths in some languages.]
  2. Thelwall, M. (in press). Author gender differences in psychology citation impact 1996-2018. International Journal of Psychology. [Female first-authored research tends to be more common and more more cited in US psychology research than US first-authored research, and females tend to work in larger teams.]
  3. Thelwall, M. (in press). Female citation impact superiority 1996-2018 in six out of seven English-speaking nations. Journal of the Association for Information Science and Technology. [Female first-authored research tends to be more cited in Australia, Canada, Ireland, Jamaica, New Zealand, UK but not the USA, although differences in all cases are small.]
  4. Thelwall, M. & Maflahi, N. (in press). Academic collaboration rates and citation associations vary substantially between countries and fields. Journal of the Association for Information Science and Technology. [There are field and international differences in whether articles with more authors attract more citations. Solo research from India and China has relatively high citation counts.]
  5. Thelwall, M. (in press). Large publishing consortia produce higher citation impact research but co-author contributions are hard to evaluate. Quantitative Science Studies. [Consortia of many authors publishing multiple articles tend to publish research with substantially above the average citation impact, but author contributions are often difficult to detect.]
  6. Thelwall, M. (2019). Are classic references cited first? An analysis of citation order within article sections. Scientometrics, 120(2), 723-731. [Initial citations in the Introduction, Background, Discussion and Conclusions may have a general agenda setting nature in many fields because they tend to be more highly cited, but may play little direct role in the new publication.] [view-only publisher version]-> scientometrics
  7. Thelwall, M. (2019). Should citations be counted separately from each originating section? Journal of Informetrics. 13(3), 658–678. [At a global scale, the standard section containing a citation (e.g., Introduction, Methods, Discussion) is not a reliable indicator of the reason why the article was cited.]
  8. Thelwall, M. (2019). The rhetorical structure of science? A multidisciplinary analysis of article headings. Journal of Informetrics. 13(3), 555–563. [There seems to be little common structure to scientific articles: no article headings are close to ubiquitous in any broad field and there are substantial field differences in the extent to which most headings are used.]
  9. Thelwall, M. (2019). The influence of highly cited papers on field normalised indicators. Scientometrics, 118(2), 519–537. [Individual outliers can influence field normalised indicators enough to influence policy decisions based on them.]
  10. Thelwall, M., Bailey, C., Makita, M., Sud, P. & Madalli, D. (2019). Gender and Research Publishing in India: Uniformly high inequality? Journal of Informetrics, 13(1), 118–131. [Female participation rates and gender differences between fields participation rates are lower in India than in the USA, and some fields have radically different gender shares.] [WIRE copy not yet active][The Wire article] [Hindu Business Online] -> scientometrics
  11. Thelwall, M., Bailey, C., Tobin, C. & Bradshaw, N. (2019). Gender differences in research areas, methods and topics: Can people and thing orientations explain the results? Journal of Informetrics, 13(1), 149-169. [There are substantial gender differences (up to a factor of 100) in the research fields, methods and topics of articles published by US academics in 2017; these cannot be fully explained by the people/thing theory of social psychology.] [WIRE copy not yet active]
  12. Mas-Bleda, A. & Thelwall, M. (2018). Do prestigious Spanish scholarly book publishers have more teaching impact? Aslib Journal of Information Management, 70(6), 673-690. DOI: 10.1108/AJIM-04-2018-0094 [Prestigious publishers in Spain tend to publish books with higher teaching impact.]
  13. Thelwall, M. (2018). Do gendered citation advantages influence field participation? Four unusual fields in the USA 1996-2017. Scientometrics,117(3), 2133-2144. [Differing rates of citing male and female first-authored research do not associate with long term changes in the gender composition of a field.]
  14. Martín-Martín, A., Orduna-Malea, E., Thelwall, M. & Delgado, E. (2018). Google Scholar, Web of Science, and Scopus: a systematic comparison of citations in 252 subject categories. Journal of Informetrics, 12(4), 1160-1177. [publisher version] [Google Scholar finds nearly all WoS (95%) and Scopus (92%) citations. The extra citations were from non-journal sources (48%-65%). Many were non-English (19%-38%).]
  15. Thelwall, M. (2018). Do females create higher impact research? Scopus citations and Mendeley readers for articles from five countries. Journal of Informetrics, 12(4), 1031-1041. [publisher version] [Nature Index article about this paper] [Female first-authored research tends to have slightly higher average citation impact in Spain, the UK and the USA, and more Mendeley readers in Spain, Turkey, the UK and the USA, but not India. At a technical level, gender comparison results depend on the field normalisation method used, and all previous analyses have arguable allowed highly cited articles to be too influential in calculations.]
  16. Thelwall, M. (2018). Dimensions: A competitor to Scopus and the Web of Science? Journal of Informetrics, 12(2), 430–435. 10.1016/j.joi.2018.03.006. [The new scholarly database Dimensions has coverage and citation counts that are similar to Scopus but it is free for many purposes.]
  17. Thelwall, M. (2018). Can Microsoft Academic be used for citation analysis of preprint archives? The case of the Social Science Research Network. Scientometrics, 115(2), 913-928. doi:10.1007/s11192-018-2704-z ][publisher read only version][Microsoft Academic has quite comprehensive coverage of preprint archives. It can be used for citation analyses of them in conjunction with a list of their publications from another source.]
  18. Kousha, K, Thelwall, M. & Abdoli, M. (2018). Can Microsoft Academic assess the early citation impact of in-press articles? A multi-discipline exploratory analysis. Journal of Informetrics, 12(1), 287-298. [wire] [Microsoft Academic can find more citations to in press articles than Scopus and seems to be a better source of early impact evidence.]
  19. Thelwall, M. (2018). Microsoft Academic automatic document searches: accuracy for journal articles and suitability for citation analysis. Journal of Informetrics, 12(1), 1-9. doi:10.1016/j.joi.2017.11.001 [Automatic Microsoft Academic journal article queries can find articles with a high level of accuracy in all fields and the resulting document set is equivalent to Scopus sets for citation analysis in most fields.]
  20. Thelwall, M. (2017). Microsoft Academic: A multidisciplinary comparison of citation counts with Scopus and Mendeley for 29 journals. Journal of Informetrics.11(4), 1201-1212. doi:10.1016/j.joi.2017.10.006 [Microsoft Academic finds 6% more citations than Scopus (and 51% more for the current year, based on 29 large journals.]
  21. Thelwall, M. (2017). Confidence intervals for normalised citation counts: Can they delimit underlying research capability? Journal of Informetrics, 11(4), 1069-1079. doi:10.1016/j.joi.2017.09.002 [local copy] [It is reasonable to use confidence intervals to estimate underlying research capability and prediction intervals to estimate future research output, although system changes over time mean that they should be used cautiously.]
  22. Thelwall, M. & Sud, P. (2018). A comparison of title words for journal articles and Wikipedia pages: Coverage and stylistic differences? El Profesional de la Información, 27(1), 49-64. doi:10.3145/epi.2018.ene.05 [Wikipedia is broadly effective at covering even specialist academic topics, but with stylistic differences.][local copy]
  23. Thelwall, M., (2018). Does Microsoft Academic find early citations? Scientometrics, 114(1), 325–334. doi:10.1007/s11192-017-2558-9 [Microsoft Academic does not have a substantial early citation advantage over Scopus for Nature, Science and seven library and information science journals.]
  24. Thelwall, M. & Fairclough, R. (2017). The research production of nations and departments: A statistical model for the share of publications. Journal of Informetrics, 11(4), 1142-1157.doi:10.1016/j.joi.2017.10.001 [Modelling the share of the world's publications from a country or department to predict future production or estimate underlying research capacity.]
  25. Thelwall, M. & Levitt, J.M.  (2018). National scientific performance evolution patterns: retrenchment, successful expansion, or overextension. Journal of the Association for Information Science and Technology, 69(5), 720-727. doi:10.1002/asi.23969 [There is a negative correlation between expansion and relative citation impact but their relationship varies by country.]
  26. Thelwall, M. & Fairclough, R. (2017). The accuracy of confidence intervals for field normalised indicators. Journal of Informetrics, 11(2), 530-540. doi:10.1016/j.joi.2017.03.004 [The MNLCS (Mean Normalised Log-transformed Citation Score) confidence interval formula is conservative for large groups but almost always safe. Bootstrap MNCS (Mean Normalised Citation Score) confidence intervals can be very unsafe, although their accuracy increases with sample sizes.] -> [softwareadditional data and graphs]
  27. Kousha, K.& Thelwall, M. (2017). News stories as evidence for research? BBC citations from articles, books and Wikipedia. Journal of the Association for Information Science and Technology, 68(8), 2017-2028. doi:10.1002/jasist.23862 [News stories provide a novel source of information about real world activities that is cited by journal articles, although news stories about research are also widely cited.]
  28. Thelwall, M. & Kousha, K. (2017). Do journal data sharing mandates work? Life sciences evidence from Dryad. Aslib Journal of Information Management, 69(1), 36-45.  doi:10.1108/AJIM-09-2016-0159 [All relevant articles share data in some life sciences journals, and the data does seem to be used, but it is not clear what it is used for.]
  29. Thelwall, M. (in press). Three practical field normalised alternative indicator formulae for research evaluation.  Journal of Informetrics. 10.1016/j.joi.2016.12.002 [A robust indicator is introduced for citation counts that allows narrower confidence intervals to be calculated for more powerful analyses. Two new proportion cited indicators are introduced to allow more powerful web indicators when a low proportion of outputs have a non-zero indicator score.]
  30. Thelwall, M. (2017). Trends in African scientific output and impact 1996-2015. African Journal of Library, Archives and Information Science, 27(2), 131-143. [African countries are increasing their share of the world’s output but mostly decreasing their relative citation impact - probably due to increasing national research.]
  31. Thelwall, M. (2017). Avoiding obscure topics and generalising findings produces higher impact research. Scientometrics, 110(1), 307-320. doi:10.1007/s11192-016-2159-z [Research with unusual words in article titles tends to be less cited than average; titles covering multiple concepts and suggesting a purpose may be more cited.]
  32. Levitt, J. & Thelwall, M. (2016). Long term productivity and collaboration in information science. Scientometrics, 108(3), 1103-1117. doi:10.1007/s11192-016-2061-8 [The long term productivity of information scientists seems to be highest if they tend to work alone or collaborate with one other author.]
  33. Thelwall, M. (2016). Citation count distributions for large monodisciplinary journals. Journal of Informetrics, 10(3), 863-874. doi:10.1016/j.joi.2016.07.006 [The discretised lognormal fits citation distributions for individual large journals better than the hooked power law, reversing the situation for entire subject categories. Ultra-high precision (128+bit) parameter fitting software for the hooked power law is also introduced.]-> scientometricscode and data for this paper.
  34. Thelwall, M. (2016). Are there too many uncited articles? Zero inflated variants of the discretised lognormal and hooked power law distributions. Journal of Informetrics, 10(2), 622-633. doi:10.1016/j.joi.2016.04.014 [Shows that sets of articles from a Scopus category can have too many uncited articles, due to the indexing of non-academic periodicals. Introduces the zero inflated discretised lognormal distribution and the zero inflated hooked power law distribution to deal with this issue, with software and data.]-> scientometricscode and data for this paper. Video 1 and 2 of the effect of modelling different numbers of uncited articles.
  35. Thelwall, M. (2016). Are the discretised lognormal and hooked power law distributions plausible for citation data? Journal of Informetrics, 10(2), 454-470. doi:10.1016/j.joi.2016.03.001. [Although the discretised lognormal and hooked power law distributions fit citation data reasonably well, they have slightly wrong shapes and it is impossible to verify that any distribution is an exact match.]->code and graphs for paper.
  36. Thelwall, M. (2016). Not dead, just resting: The practical value of per publication citation indicators. Journal of Informetrics, 10(2), 667-670. doi:10.1016/j.joi.2015.12.009
  37. Thelwall, M. & Thelwall, S. (2016). Development studies research 1975-2014 in academic journal articles: The end of economics? El Profesional de la Información, 25(1), 47-58.
  38. Thelwall, M. (2016). The discretised lognormal and hooked power law distributions for complete citation data: Best options for modelling and regression. Journal of Informetrics, 10(2), 336-346. doi:10.1016/j.joi.2015.12.007. [For regression analyses, the best option is to use ordinary least squares regression applied to the natural logarithm of citation counts plus one.code, data and results for paper.
  39. Thelwall, M. (in press). The precision of the arithmetic mean, geometric mean and percentiles for citation data: An experimental simulation modelling approach. Journal of Informetrics, 10(1), 110-123. doi:10.1016/j.joi.2015.12.001 [The geometric mean is more precise than the arithmetic mean and percentile indicators, at least for sets of articles with the same lognormal scale parameter, and so is preferable for citation indicators unless heavier weighting is needed to be given to more highly cited articles.] -> code and results for the papergeometric mean simple explanation blog post.
  40. Thelwall, M., & Sud, P. (2016). National, disciplinary and temporal variations in the extent to which articles with more authors have more impact: Evidence from a geometric field normalised citation indicator. Journal of Informetrics, 10(1), 48-61. doi:10.1016/j.joi.2015.11.007 [The geometric Mean Normalized Citation Score is introduced to compare sets of articles from different fields and years. Domestic journal articles with more authors tend to have more citations, but with disciplinary, national and temporal differences and the exception of Russia.] -> geometric mean simple explanation blog post.
  41. Li, X., Thelwall, M. & Kousha, K. (2015). The role of arXiv, RePEc, SSRN and PMC in formal scholarly communication, Aslib Journal of Information Management, 67(6), 614 - 635. [Subject repositories attract substantial numbers of citations from subject areas outside of their primary focus.]
  42. Fairclough, R., & Thelwall, M. (2015). More precise methods for national research citation impact comparisons. Journal of Informetrics, 9(4), 895-906. doi:10.1016/j.joi.2015.09.005 [The geometric mean is the most precise indicator of citation impact for a nation's research within a single field, followed by the percentage in the top 50% and then the arithmetic mean. Percentages in the top 10% and 1% are relatively imprecise indicators, as are regression parameters.geometric mean simple explanation blog post.
  43. Kousha, K. & Thelwall, M. (2017). Patent citation analysis with Google. Journal of the Association for Information Science and Technology, 68(1), 48-61. doi:10.1002/asi.23608 [Citations from patents to academic papers can be extracted semi-automatically from the Google Patents index and the results give evidence of commercial relevance for a varying minority of articles in applied disciplines.]
  44. Thelwall, M. (2015). Are medical articles highlighting detailed statistics more cited? Anales de Documentacion, 18(2), doi:10.6018/analesdoc.18.2.%20225201 [Medical articles mentioning more detailed statistics tend to be more cited.]
  45. Thelwall, M. & Fairclough, R. (2015). The influence of time and discipline on the magnitude of correlations between citation counts and quality scores. Journal of Informetrics, 9(3), 529–541. doi:10.1016/j.joi.2015.05.006 [Mixing sets of articles from different years or fields can substantially reduce the strength of the correlation between quality and citation counts.] [data and software]
  46. Thelwall, M. & Fairclough, R. (2015). Geometric journal impact factors correcting for individual highly cited articles. Journal of Informetrics, 9(2),263–272. [Shows that using the geometric mean rather than the arithmetic mean in journal impact factors reduces the impact of individual highly cited articles, although the differences are not large.]
  47. Thelwall, M. & Wilson, P. (2016). Does research with statistics have more impact? The citation rank advantage of structural equation modelling. Journal of the Association for Information Science and Technology, 67(5), 1233–1244. doi:10.1002/asi.23474 [Articles using structural equation modelling and related methods tend to get more citations and more Mendeley readers than do comparable articles, although the extent varies by discipline and technique.]
  48. Minguillo, D. & Thelwall, M. (2015). Research excellence and university-industry collaboration in UK science parks. Research Evaluation, 24(2), 181-196.
  49. Minguillo, D., & Thelwall, M. (2015). Which are the best innovation support infrastructures for universities? Evidence from R&D output and commercial activities. Scientometrics, 102(1), 1057-1081.
  50. Minguillo, D., Tijssen, R. & Thelwall, M. (2015). Do science parks promote research and technology? A scientometric analysis of the UK. Scientometrics, 102(1), 701-725.
  51. Thelwall, M. & Wilson, P. (2014). Regression for citation data: An evaluation of different methods. Journal of Informetrics. , 8(4), 963–971. [The best regression strategy for citation is to add 1, take the log and then use ordinary least squares regression.]
  52. Thelwall, M. & Maflahi, N. (2015). How important is computing technology for library and information science research? Library and Information Science Research, 37(1), 42–50. [About two thirds of library and information science articles explicitly mention an aspect of computing in their title, keywords or abstract, showing the widespread importance of computing technology to the discipline.]
  53. Thelwall, M. & Wilson, P. (2014). Distributions for cited articles from individual subjects and years. Journal of Informetrics, 8(4), 824-839. [Shows that for a set of articles from a single subject and year, the hooked power law and the lognormal distributions fit better than the power law (for articles with at least one citation), even for the distribution tail, and so should always be used in preference to the power law.]
  54. Sud, P. & Thelwall, M. (2016). Not all international collaboration is beneficial: The Mendeley readership and citation impact of biochemical research collaboration.  Journal of the Association for Information Science and Technology, 67(8), 1849-1857. doi:10.1002/asi.2351 [Although the benefits of international collaboration seem to be an accepted truth throughout science, this article suggests that collaboration with some countries may not be beneficial, at least at a statistical level.]
  55. Didegah, F., & Thelwall, M. (2013). Which factors help authors produce the highest impact research? Collaboration, journal and document properties. Journal of Informetrics, 7(4), 861-873
  56. Levitt, J., & Thelwall, M. (2013). Alphabetization and the skewing of first authorship towards last names early in the alphabet. Journal of Informetrics 7(3), 575– 582.
  57. Didegah, F., & Thelwall, M. (2013). Determinants of research citation impact in nanoscience and nanotechnology. Journal of the American Society for Information Science and Technology64(5), 1055–1064.
  58. Didegah, F., Thelwall, M. & Gazni, A. (2012). An international comparison of journal publishing and citing behaviours, Journal of Informetrics 6(4), 516-531.
  59. Levitt, J., Thelwall, M. & Oppenheim, C. (2011). Variations between subjects in the extent to which the social sciences have become more interdisciplinary. Journal of the American Society for Information Science and Technology, 62(6), 1118–1129
  60. Levitt, J., & Thelwall, M. (2011). A combined bibliometric indicator to predict article impact. Information Processing & Management, 47(2), 300-308
  61. Levitt, J., & Thelwall, M. (2010). Does the higher citation of collaborative research differ from region to region? A case study of economics, Scientometrics., 85(1), 171-183.
  62. Levitt, J., & Thelwall, M. (2009). The most highly cited library and information science articles: Interdisciplinarity, first authors and citation patterns. Scientometrics, 78(1), 45-67.
  63. Levitt, J., & Thelwall, M. (2009). Citation levels and collaboration within Library and Information Science, Journal of the American Society for Information Science and Technology, 60(3), 434-442.
  64. Levitt, J., & Thelwall, M. (2008). Is multidisciplinary research more highly cited? A macro-level study. Journal of the American Society for Information Science and Technology, 59(12), 1973-1984.
  65. Levitt, J., & Thelwall, M. (2008). Patterns of annual citation of highly cited articles and the prediction of their citation ranking: A comparison across subjects, Scientometrics, 77(1), 41-60.

Sentiment Analysis

  1. Pillai, R. G., Thelwall, M., & Orasan, C. (2018). Trouble on the road: Finding reasons for commuter stress from tweets. In Proceedings of the Workshop on Intelligent Interactive Systems and Language Generation (2IS&NLG) (pp. 20-25).
  2. Pillai, R. G., Thelwall, M., & Orasan, C. (2018). What makes you stressed? Finding reasons from tweets. In Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis (pp. 266-272).
  3. Pillai, R., Thelwall, M. & Orasan, C. (2018). Detection of stress and relaxation magnitudes for tweets. SocialNLP Workshop, WWW2018 Conference. Lyon, France (pp. 1677-1684).
  4. Culpeper, J., Findlay, A., Cortese, B. & Thelwall, M. (2018). Measuring emotional temperatures in Shakespeare’s drama. English Text Construction, 11(1), 10-37.
  5. Thelwall, M. (2018). Gender bias in machine learning for sentiment analysis. Online Information Review, 42(3), 343-354. doi: 10.1108/OIR-05-2017-0152 [Data sets from this paper] [Machine learning sentiment analysis over-represents the opinions of females even if they are equally represented in training data.]
  6. Thelwall, M. (2018). Gender bias in sentiment analysis. Online Information Review, 42(1), 45-57. [publisher version]. doi:10.1108/OIR-05-2017-0139 [Lexical sentiment analysis over-represents the opinions of females because they express sentiment more clearly.]
  7. Paltoglou, G. & Thelwall, M. (2017). Sensing social media: A range of approaches for sentiment analysis. In: Holyst, J. (Ed.) Cyberemotions: Collective emotions in cyberspace. Berlin, Germany: Springer (pp. 97-117). doi: 10.1007/978-3-319-43639-5_6 [Sentiment analysis methods for social media texts - review.]
  8. Thelwall, M. (2017). The heart and soul of the web? Sentiment strength detection in the social web with SentiStrength. In: Holyst, J. (Ed.) Cyberemotions: Collective emotions in cyberspace. Berlin, Germany: Springer (pp. 119-134). doi:10.1007/978-3-319-43639-5_7 [Summary of SentiStrength sentiment analysis capabilities.]
  9. Thelwall, M. (2017). TensiStrength: Stress and relaxation magnitude detection for social media texts. Information Processing & Management, 53(1), 106–121. doi:10.1016/j.ipm.2016.06.009 [Introduces a program TensiStrength to estimate the strength of stress and relaxation expressed in social media
  10. Vilares Calvo, D., Thelwall, M., & Alonso, M.A. (2015). The megaphone of the people? Spanish SentiStrength for real-time analysis of political tweets. Journal of Information Science, 41(6), 799-813. [Introduces an improved Spanish sentiment strength detection version of SentiStrength and shows Spanish political tweets tend to amplify news stories.]
  11. Thelwall, M., & Buckley, K. (2013). Topic-based sentiment analysis for the Social Web: The role of mood and issue-related words. Journal of the American Society for Information Science and Technology, 64(8), 1608–1617.
  12. Thelwall, M., Buckley, K., & Paltoglou, G., Marcin Skowron, David Garcia, Stephane Gobron, Junghyun Ahn, Arvid Kappas, Dennis Küster, and Janusz A. Holyst (2013). Damping sentiment analysis in online communication: Discussions, monologs and dialogs. In: A. Gelbukh (Ed.): CICLing 2013, Part II, LNCS 7817, pp. 1-12. Springer, Heidelberg.
  13. Paltoglou, G., & Thelwall, M. (2013). Seeing stars of valence and arousal in blog posts, IEEE Transactions on Affective Computing, 4(1), 116-123.
  14. Paltoglou, G., Theunis, M., Kappas, A., & Thelwall, M. (2013). Predicting emotional responses to long informal text, IEEE Transactions on Affective Computing, 4(1),106 -115.
  15. Ponomareva, N., & Thelwall, M. (2012). Do neighbours help? An exploration of graph-based algorithms for cross-domain sentiment classification. EMNLP-CoNLL 2012, 655-665.
  16. Ponomareva, N., & Thelwall, M. (2012). Biographies or blenders: Which resource is best for cross-domain sentiment analysis? CiCLing 2012, Delhi, India and Lecture Notes in Computer Science 7181/2012, 488-499.
  17. Thelwall, M., Buckley, K., & Paltoglou, G. (2012). Sentiment strength detection for the social Web. Journal of the American Society for Information Science and Technology, 63(1), 163-173.
  18. Gobron, S., Ahn, J., Paltoglou, G., Thelwall, M. & Thalmann, D. (2010). From sentence to emotion: A real-time three-dimensional graphics metaphor of emotions extracted from text. The Visual Computer: International Journal of Computer Graphics, 26(6-8), 505-519.
  19. Thelwall, M., Buckley, K., Paltoglou, G. Cai, D., & Kappas, A. (2010). Sentiment strength detection in short informal text. Journal of the American Society for Information Science and Technology, 61(12), 2544–2558
  20. Prabowo, R., Thelwall, M. (2009). Sentiment analysis: A combined approach, Journal of Informetrics, 3(1), 143-157.


  1. Makita, M., Mas-Bleda, A., Stuart, E., & Thelwall, M. (2019). Ageing, old age and older adults: a social media analysis of dominant topics and discourses. Ageing & Society, DOI: 10.1017/S0144686X19001016 [Twitter reproduces the ageist language of tradiational media.]
  2. Thelwall, M. & Cugelman, B. (2017). Monitoring Twitter strategies to discover resonating topics: the case of the UNDP. El Profesional de la Información, 26(4), 649-661. [local copy]
  3. Thelwall, M. (2015). Evaluating the comprehensiveness of Twitter Search API results: A four step method. Cybermetrics,18-19, p1.
  4. Wilkinson, D. & Thelwall, M. (2012). Trending Twitter topics in English: An international comparison. Journal of the American Society for Information Science and Technology, 63(8), 1631-1646. PowerPoint results map; bottom-up resultstop-down results.
  5. Thelwall, M., Buckley, K., & Paltoglou, G. (2011). Sentiment in Twitter events. Journal of the American Society for Information Science and Technology, 62(2), 406-418. [Peaks of interest in external events are reflected in slight increases in negative sentiment strength for the topic.]


  1. Thelwall, M. (2018). Can museums find male or female audiences online with YouTube? Aslib Journal of Information Management, 70(5), 481-497. [There are huge gender differences in the audiences of museum YouTube channels, including for museums of the same broad type. Museums can target audiences by gender through YouTube.]
  2. Thelwall, M. & Mas-Bleda, A. (2018). YouTube science channel video presenters and comments: Female friendly or vestiges of sexism? Aslib Journal of Information Management, 70(1), 28-46. doi:10.1108/AJIM-09-2017-0204 [Popular science channel comments tend to be dominated by males and tend not to be negative towards, females although there is a minority of sexist commenting. Presenter gender does not seem to influence audience gender.] [Note that The method used to detect gender gives a small bias in favour of males. After removing this bias, the Tyler DeWitt channel has 3% more female than male commenters, but all the other channels have a majority of male commenters. See also gender detection accuracy calculations]
  3. Thelwall, M. (2018). Social media analytics for YouTube comments: Potential and limitations. International Journal of Social Research Methodology, 21(3), 303-316. doi:10.1080/13645579.2017.1381821. [author original preprint, publisher limited time free link] [Introduces a method and software to identify plausible patterns of subtopic difference, gender and sentiment.]
  4. Thelwall, M., Kousha, K., Weller, K., & Puschmann, C. (2012). Assessing the impact of online academic videos. In: G. Widen Wulff & K. Holmberg, (Eds), Social Information Research, Bradford: Emerald Group Publishing Limited. (pp. 195-213).
  5. Kousha, K., Thelwall, & Abdoli, M. (2012). The role of online videos in research communication: A content analysis of YouTube videos cited in academic publications. Journal of the American Society for Information Science and Technology, 63(9), 1710–1727.
  6. Sugimoto, C.R. & Thelwall, M. (2013). Scholars on soap boxes: Science communication and dissemination via TED videos. Journal of the American Society for Information Science and Technology64(4), 663-674
  7. Thelwall, M., Sud, P., & Vis, F. (2012). Commenting on YouTube videos: From Guatemalan rock to El Big Bang. Journal of the American Society for Information Science and Technology, 63(3), 616–629.

Social web

  1. Stuart, E., Thelwall, M., & Stuart, D. (2019). Which image types do universities tweet? First Monday, 24(3). [Universities typically tweet showcasing and humanising images, apparently broadcasting to potential future students.]
  2. Thelwall, M. & Bourrier, K. (2019). The reading background of Goodreads book club members: A female fiction canon?  Journal of Documentation, 75(5), 1139-1161. doi:10.1108/JD-10-2018-0172 [Female authors dominate the core reading of reading group members in Goodreads.]
  3. Thelwall, M. & Stuart, E. (2019). She’s Reddit: A source of statistically significant gendered interest information? Information Processing & Management, 56(4), 1543-1558. [Gender differences in interests can be detected by subreddit choices or subreddit comments.]
  4. Thelwall, M. (2018). Can social news websites pay for content and curation? The SteemIt cryptocurrency model. Journal of Information Science, 44(6), 736–751. [The post reward system in SteemIt seems to encourage network building in the form of personal sentiment-rich introductions rather than content, at least for first posts.]
  5. Thelwall, M. & Vis, F. (2017). Gender and image sharing on Facebook, Twitter, Instagram, Snapchat and WhatsApp in the UK: Hobbying alone or filtering for friends? Aslib Journal of Information Management, 69(6), 702-720. [Gender-related results of a survey of UK social media image sharing.]
  6. Stuart, E., Stuart, D. & Thelwall, M. (2017). An investigation of the online presence of UK universities on Instagram. Online Information Review, 41(5), 582-597. doi:10.1108/OIR-02-2016-0057
  7. Thelwall, M. (2019). Reader and author gender and genre in Goodreads. Journal of Librarianship & Information Science, 5(2), 403-430. [In most Goodreads genres, reviewers give higher ratings to books authored by their own gender. Readers and authors also seem to value gendered aspects of books, even in non-gendered genres.]
  8. Thelwall, M. (2016). Does astronomy research become too dated for the public? Wikipedia citations to astronomy and astrophysics journal articles 1996-2014. El Profesional de la Información, 25(6), 893-900. doi:10.3145/epi.2016.nov.06 [Astronomy and astrophysics journal articles from before 2008 tend to be more cited than newer articles, suggesting stabilisation in Wikipedia content.]
  9. Thelwall, M. (2017). Book genre and author gender: romance>paranormal-romance to autobiography>memoir. Journal of the Association for Information Science and Technology, 68(5), 1212-1223. 10.1002/asi.23768. [There are gender differences in authorship in almost all genres and gender differences the level of interest in, and ratings of, books in a minority of genres. There is not a clear relationship between the success of an author's gender and the prevalence of that gender within a genre.]
  10. Thelwall, M. & Kousha, K. (2017). Goodreads: A social network site for book readers. Journal of the Association for Information Science and Technology, 68(4), 972-983. doi:10.1002/asi.23733 [Goodreads users are predominantly female. Members choose their own combinations of book-related and social networking activities within the site.]
  11. Thelwall, M., Goriunova, O. Vis, F., Faulkner, S., Burns, A., Aulich, J. Mas-Bleda, A., Stuart, E. & D’Orazio, F. (2016). Chatting through pictures? A classification of images tweeted in one week in the UK and USA. Journal of the Association for Information Science and Technology, 67(11), 2575-2586. [People tend to share photographs more than other types of images on Twitter, often apparently in real time, and often of people, including selfies. Layered or hybrid images are also common, such as screenshots, collages, and captioned pictures, even for routine sharing.]
  12. Thelwall, M. & Kappas, A. (2014). The role of sentiment in the social web. In: von Scheve, C. & Salmela, M. (eds.) Collective Emotions. Oxford: Oxford University Press (pp. 375-388).
  13. Thelwall, M. & Kousha, K. (2014). Social network or academic network?. Journal of the American Society for Information Science and Technology, 65(4), 721-731.
  14. Thelwall, M. (2011). Privacy and gender in the Social Web. In: Sabine Trepte, Leonard Reinecke (Eds), Privacy online: Perspectives on Privacy and Self-Disclosure in the Social Web, New York: Springer (pp. 255-269).
    • Chapter summary: Gender is important for understanding attitudes to privacy in the social web because of the many gender-related privacy differences. In general, women are more concerned about privacy than men but nevertheless publish more personal information in blogs and social network sites. The root causes of the differences seem to lie in socialised gendered communication strategies and privacy-related issues that disproportionately concern women. This chapter reviews evidence for gendered online communication and privacy concerns, focusing mainly on blogs, social network sites and YouTube, and includes a special section on LGBT issues. [See also book web site]
  15. Wilkinson, D. & Thelwall, M. (2010). Social network site changes over time: The case of MySpaceJournal of the American Society for Information Science and Technology, 61(11), 2311–2323.
  16. Thelwall, M. & Wilkinson, D. (2010). Public dialogs in social network sites: What is their purpose?, Journal of the American Society for Information Science and Technology, 61(2), 392-404.
  17. Thelwall, M., Wilkinson, D. & Uppal, S.(2010). Data mining emotion in social network communication: Gender differences in MySpace, Journal of the American Society for Information Science and Technology, 61(1), 190-199.[Two thirds of comments in US MySpace expressed positive sentiment but a minority (20%) contained negative sentiment; females are likely to give and receive more positive comments than are males.]
  18. Thelwall, M. (2010). Emotion homophily in social network site messages. First Monday, 15, (4).
  19. Thelwall, M. (2009). Social network sites: Users and uses. In: M. Zelkowitz (Ed.), Advances in Computers 76. Amsterdam: Elsevier (pp. 19-73). (email for a preprint).
  20. Thelwall, M. (2009, to appear). Homophily in MySpace, Journal of the American Society for Information Science and Technology.
  21. Thelwall, M. (2009). MySpace comments. Online Information Review, 33(1), 58-76.
  22. Thelwall, M. (2008). No place for news in social networking web sites? Online Information Review, 32(6), 726-744.
  23. Thelwall, M. (2008). How are social network sites embedded in the web? An exploratory link analysis. Cybermetrics. 12(1), paper 1.
  24. Thelwall, M. (2008). Text in social network web sites: A word frequency analysis of Live Spaces, First Monday 13(2).
  25. Thelwall, M. (2008). Social networks, gender and friending: An analysis of MySpace member profiles, Journal of the American Society for Information Science and Technology, 59(8), 1321-1330.
  26. Thelwall, M. (2008). Fk yea I swear: Cursing and gender in a corpus of MySpace pages, Corpora, 3(1), 83-107. Preprint (with extended literature review and background information compared to the published version, and a revised first two paragraphs of the conclusion [8 Jan, 2008]) available at:
  27. Thelwall, M. (2008). Social networks, gender and friending: An analysis of MySpace member profiles, Journal of the American Society for Information Science and Technology, 59(8), 1321-1330.

Blogs and RSS

  1. Koteyko, N. Thelwall, M. & Nerlich, B. (2010). From carbon markets to carbon morality: creative compounds as framing devices in online discourses on climate change mitigation, Science Communication, 32(1), 25-54.
  2. Prabowo, R., Thelwall, M., Hellsten I., & Scharnhorst A., (2008). Evolving debate in online communication: A graph analytical approach, Internet Research.18(5), 520-540.
  3. Park, H. W., & Thelwall, M. (2008). Developing network indicators for ideological landscapes from the political blogosphere in South Korea, Journal of Computer-Mediated Communication, 13(4), 856-879.
  4. Thelwall, M. & Hasler, L. (2007). Blog search engines. Online Information Review, 31(4), 467-479.
  5. Thelwall, M., Byrne, A. & Goody, M. (2007). Which types of news story attract bloggers? Information Research 12(4).
  6. Lamboitte, R., Ausloos, M. & Thelwall, M. (2007). Word statistics in Blogs and RSS feeds: Towards empirical universal evidence. Journal of Informetrics, 1(4), 277-286.
  7. Prabowo, R., Thelwall, M. & Alexandrov, M. (2007). Generating overview timelines for major events in an RSS corpus. Journal of Informetrics.
  8. Thelwall, M. (2007). Blog searching: The first general-purpose source of retrospective public opinion in the social sciences? Online Information Review, 31(3), 277-289.
  9. Thelwall, M. & Stuart, D. (2007). RUOK? Communication technologies blogged during a crisis. Journal of Computer-Mediated Communication 12(9).
  10. Thelwall, M. & Prabowo, R. (2007). Identifying and characterinsing public science-related fears from RSS feeds. Journal of the American Society for Information Science and Technology, 58(3), 379-390.
  11. Thelwall, M. & Hellsten, I. (2006). The BBC, Telegraph and Wikinews timelines of the London Attacks: A comparison with contemporary discussions. Information Research 12(1).
  12. Thelwall, M. (2006). Bloggers during the London attacks: Top information sources and topics. WWW2006 blog workshop,
  13. Prabowo, R. & Thelwall, M. (2006). A comparison of feature selection methods for an evolving RSS feed corpus. Information Processing & Management (Informetrics special issue), 42(6), 1491-1512.
  14. Thelwall, M., Prabowo, R. & Fairclough, R. (2006). Are raw RSS feeds suitable for broad issue scanning? A science concern case study. Journal of the American Society for Information Science and Technology, 57(12), 1644-1654.
  15. Thelwall, M. & Price, E. (2006). Language evolution and the spread of ideas: A procedure for identifying emergent hybrid word family members. Journal of the American Society for Information Science and Technology, 57(10), 1326-1337.


  1. Angus, E., Thelwall, M. (2010). Motivations for image publishing and tagging on Flickr. In Turid Hedlund and Yasar Tonta (Eds.), Proceedings of the 14th International Conference on Electronic Publishing. (pp. 189 - 204). Helsinki: Hanken School of Economics.
  2. Angus, E., Thelwall, M., Stuart, D. (2010). Flickr’s potential as an academic image resource: an exploratory study. Journal of Librarianship and Information Science,42(4) 268–278.
  3. Angus, E., Stuart D., & Thelwall, M. Flickr, (2008). General patterns of tag usage among university groups in Flickr, Online Information Review, 32(1), 89-101.

Link Analysis Methods

  1. Sud, P. & Thelwall, M. (2014). Linked title mentions: A new automated link search candidate. Scientometrics, 101(3), 1831-1849. [Introduces a new automatic link search method that is in Webometric Analyst and can give more accurate results that URL citations or title mentions in certain circumstances.]
  2. Thelwall, M., Sud, P., & Wilkinson, D. (2012). Link and co-inlink network diagrams with URL citations or title mentions. Journal of the American Society for Information Science and Technology, 63(4),805-816
  3. Thelwall, M. & Sud, P. (2011). A comparison of methods for collecting web citation data for academic organisations. Journal of the American Society for Information Science and Technology, 62(8), 1488–1497. [Compares URL citations, title mentions and link counts against each other, for both hit count estimates and full lists of URLs.]
  4. Thelwall, M. & Wilkinson, D. (2008). A generic lexical URL segmentation framework for counting links, colinks or URLs, Library and Information Science Research, 30(2), 94-101. [publisher's final version]
  5. Thelwall, M. (2006). Interpreting social science link analysis research: A theoretical framework. Journal of the American Society for Information Science and Technology, 57(1), 60-68.
  6. Thelwall, M. (2004). Weak benchmarking indicators for formative and semi-evaluative assessment of research. Research Evaluation, 13(1), 63-68.
  7. Payne, N., & Thelwall, M. (2005). Mathematical models for academic Webs: Linear relationship or non-linear power law? Information Processing & Management, 41(6), 1495-1510.
  8. Thelwall, M. (2004). Methods for reporting on the targets of links from national systems of university Web sites. Information Processing & Management, 40(1), 125-144.
  9. Thelwall, M. & Wilkinson, D. (2003). Three target document range metrics for university Web sites. Journal of the American Society for Information Science and Technology, 54(6), 489-496.
  10. Thelwall, M. (2003). Web use and peer interconnectivity metrics for academic web sites, Journal of Information Science, 29(1), 11-20.
  11. Thelwall, M. (2002). Conceptualizing documentation on the Web: an evaluation of different heuristic-based models for counting links between university web sites, Journal of the American Society for Information Science and Technology, 53(12), 995-1005. [Cited in Microsoft patent: US 7739281 B2]
  12. Thelwall, M. (2002). Sources of links for WIF calculations, Journal of Documentation 58(1) 60-72.
  13. Thelwall, M. (2001). Extracting macroscopic information from web links, Journal of the American Society for Information Science and Technology, 52(13), 1157-1168.
  14. Thelwall, M. (2001). Exploring the link structure of the web with network diagrams, Journal of Information Science 27(6) 393-402.
  15. Thelwall, M. (2004). Web citation analysis. Emerald Link Learning Curve.
  16. Thelwall, M. (2004). Link analysis: An information science approach. San Diego: Academic Press.
    • Also available translated into Chinese as: (美) 迈克·塞沃尔著 (2009). 链接分析:信息科学的研究方法, 东南大学出版社 (South East University Press). ISBN 978-7-5641-1279-0.
  17. Thelwall, M. (2005). Data cleansing and validation for Multiple Site Link Structure Analysis. In: Scime, A. (Ed.), Web Mining: Applications and Techniques. Idea Group Inc, pp. 208-227.

Factors Influencing Link Creation

  1. Holmberg, K. & Thelwall, M. (2009). Local government web sites in Finland: A geographic and webometric analysis, Scientometrics 79(1), 157-169.
  2. Harries, G., Wilkinson, D., Price, E., Fairclough, R. & Thelwall, M. (2004). Hyperlinks as a data source for science mapping, Journal of Information Science, 30(5), 436-447.
  3. Thelwall, M., & Harries, G. (2004). Do better scholars’ web publications have significantly higher online impact? Journal of the American Society for Information Science and Technology, 55(2), 149-159.
  4. Thelwall, M. (2003). What is this link doing here? Beginning a fine-grained process of identifying reasons for academic hyperlink creation, Information research, 8(3).
  5. Thelwall, M., Vaughan, L., Cothey, V., Li, X. & Smith, A. (2003). Which academic subjects have most online impact? A pilot study and a new classification process, Online Information Review 27(5), 333-343.
  6. Thelwall, M., Harries, G., & Wilkinson, D. (2003). Why do web sites from different academic subjects interlink? Journal of Information Science, 29(6), 445-463.
  7. Thelwall, M. & Harries, G. (2003). The connection between the research of a University and Counts of Links to its Web Pages: An investigation based upon a classification of the relationships of pages to the research of the host university. Journal of the American Society for Information Science and Technology, 54(7), 594-602.
  8. Thelwall, M., Tang, R. & Price, E. (2003). Linguistic patterns of academic web use in Western Europe, Scientometrics, 56(3), 417-432.
  9. Vaughan, L. & Thelwall, M. (2003). Scholarly use of the web: What are the key inducers of links to journal web sites? Journal of the American Society for Information Science and Technology, 54(1), 29-38.
  10. Wilkinson, D., Harries, G., Thelwall, M. & Price, E. (2003). Motivations for academic web site interlinking: Evidence for the web as a novel source of information on informal scholarly communication, Journal of Information Science, 29(1), 29(1), 59-66.
  11. Thelwall, M. (2002). A research and institutional size based model for national university web site interlinking, Journal of Documentation, 58(6), 683-694.
  12. Thelwall, M. (2002). The top 100 linked pages on UK university web sites: high inlink counts are not usually directly associated with quality scholarly content, Journal of Information Science, 28(6), 485-493.
  13. Thelwall, M. (2002). Evidence for the existence of geographic trends in university web site interlinking, Journal of Documentation, 58(5), 563-574.

Longitudinal Link Analysis

  1. Payne, N. & Thelwall, M. (2009). A longitudinal analysis of Alternative Document Models. ASLIB Proceedings., 61(1), 101-116.
  2. Payne, N., & Thelwall, M. (2008). Do academic link types change over time?, Journal of Documentation, 64(5), 707-720.
  3. Payne, N. & Thelwall, M. (2008). Longitudinal trends in academic web links. Journal of Information Science, 34(1), 3-14.
  4. Payne, N. & Thelwall, M. (2007). A longitudinal study of academic webs: Growth and stabilisation, Scientometrics, 71(3), 523-539

General Link Analysis

  1. Scharnhorst, A. & Thelwall, M., (2005). Citation and hyperlink networks. Current Science, 89(9), 1518-1523.

Political Link Analysis

  1. Park, H. W. & Thelwall, M. (2008). Web linkage pattern and social structure using politicians’ websites in South Korea. Quality & Quantity, 42(6), 687-697
  2. Park, H.W., Thelwall, M. & Kluver, R. (2005). Political hyperlinking in South Korea: Technical indicators of ideology and content, Sociological Research Online.

Commercial-Academic Link Analysis

  1. Stuart, D. & Thelwall, M. (2006). Investigating triple helix relationships using URL citations: A case study of the UK West Midlands automobile industry. Research Evaluation , 15(2), 97-106 .
  2. Stuart, D., Thelwall, M. & Harries, G. (2007). UK academic web links and collaboration – an exploratory study. Journal of Information Science, 33(2), 231-246.
  3. Thelwall, M. & Harries, G. (2004). Can personal web pages that link to universities yield information about the wider dissemination of research? Journal of Information Science, 30(3), 243-256.
  4. Thelwall, M. (2004). Can the web give useful information about commercial uses of scientific research? Online Information Review, 28(2), 120-130.

Digital Library Link Analysis

  1. Zuccala, A., Thelwall, M., Oppenheim, C., & Dhiensa, R. (2008, to appear). Web Intelligence Analyses of Digital Libraries: A Case Study of the National electronic Library for Health (NeLH). Journal of Documentation.

Journal Link Analysis

  1. Kim, H., Park, H.W., & Thelwall, M. (2006). Comparing academic hyperlink structures with journal publishing in Korea: A social network analysis, Science Communication, 27(4), 540-564

Link Analysis Case Studies

  1. Kousha, K. & Thelwall, M. (2014). Disseminating Research with Web CV Hyperlinks. Journal of the Association for Information Science and Technology, 65(8), 1615–1626. [Shows that few EU researchers are fully exploiting their CVs to publicise their research.]
  2. Mas Bleda, A., Thelwall, M., Kousha, K., & Aguillo, I. (2014). Successful researchers publicizing research online: An outlink analysis of European highly cited scientists’ personal Websites, Journal of Documentation, 70(1), 148-172
  3. Minguillo, D. & Thelwall, M. (2012) Mapping the network structure of science parks: An exploratory study of cross-sectoral interactions reflected on the web, Aslib Proceedings, 64(4), 332-357.
  4. Thelwall, M., Klitkou, A., Verbeek, A., Stuart, D. & Vincent, C. (2010). Policy-relevant webometrics for individual scientific fields. Journal of the American Society for Information Science and Technology, 61(7) 1464-1475
  5. Holmberg, K. & Thelwall, M. (2008, to appear). Local government web sites in Finland: A geographic and webometric analysis, Scientometrics.
  6. Barjak, F. & Thelwall, M. (2008). A statistical analysis of the web presences of European life sciences research teams. Journal of the American Society for Information Science and Technology.59(4), 628-643.
  7. Thelwall, M., Li, X., Barjak, F. & Robinson, S. (2008). Assessing the web connectivity of research groups on an international scale. ASLIB Proceedings.60(1), 18-31.
  8. Barjak, F., Li., X. & Thelwall, M. (2007). Which factors explain the web impact of scientists’ personal homepages? Journal of the American Society for Information Science and Technology 58(2), 200-211.
  9. Li, X., Thelwall, M., Musgrove, P. & Wilkinson, D. (2005). National and international university departmental web site interlinking: Part 1, validation of departmental link analysis. Scientometrics, 64(2), 151-185.
  10. Li, X., Thelwall, M., Musgrove, P. & Wilkinson, D. (2005). National and international university departmental web site interlinking: Part 2, link patterns. Scientometrics, 64(2), 187-208.
  11. Park, H. & Thelwall, M. (2006). Web science communication in the age of globalization: Links among universities’ websites in Asia and Europe. New Media & Society, 8(4), 631-652
  12. Payne, N. & Thelwall, M. (2004). A statistical analysis of UK academic web links. Cybermetrics. 8(1).
  13. Tang, R. & Thelwall, M. (2008). A hyperlink analysis of US public and academic libraries’ Web sites, Library Quarterly, 78(4), 419-435.
  14. Vaughan, L. & Thelwall, M. (2005). A modeling approach to uncover hyperlink patterns: The case of Canadian universities. Information Processing & Management, 41(2), 347-359.
  15. Tang, R. & Thelwall, M. (2004). Patterns of national and international web inlinks to US academic departments: An analysis of disciplinary variations. Scientometrics, 60(3), 475-485.
  16. Thelwall, M. & Tang, R. (2003). Disciplinary and linguistic considerations for academic Web linking: An exploratory hyperlink mediated study with Mainland China and Taiwan, Scientometrics, 58(1), 153-179.
  17. Thelwall, M., & Aguillo, I. (2003). La salud de las Web universitarias españolas, Revista Española de Documentación Científica, 26(3), 291-305.
  18. Thelwall, M. & Price, E. (2003). Disciplinary differences in academic web presence – A statistical study of the UK. Libri, 53(4), 242-253.
  19. Tang, R. & Thelwall, M. (2003). Disciplinary differences in US academic departmental web site interlinking, Library & Information Science Research, 25(4), 437-458.
  20. Li, X., Thelwall, M., Musgrove, P. & Wilkinson, D. (2003). The relationship between the links/Web Impact Factors of computer science departments in UK and their RAE (Research Assessment Exercise) ranking in 2001, Scientometrics, 57(2), 239-255.
  21. Thelwall, M. & Wilkinson, D. (2003). Graph structure in three national academic Webs: Power laws with anomalies, Journal of the American Society for Information Science and Technology, 54(8), 706-712.
  22. Thelwall, M. & Smith, A. (2002). A study of the interlinking between Asia-Pacific university web sites, Scientometrics, 55(3), 335-348.
  23. Smith, A. & Thelwall, M. (2002). Web Impact Factors for Australasian universities, Scientometrics, 54(1-2), 363-380.
  24. Thelwall, M. Binns, R. Harries, G. Page-Kennedy, T. Price E. and Wilkinson, D. (2002). European Union associated university websites, Scientometrics, 53(1), 95-111.
  25. Thelwall, M. (2002). An initial exploration of the link relationship between UK university web sites, ASLIB Proceedings, 54(2), 118-126.
  26. Chu, H., He, S. & Thelwall, M. (2002). Library and information science schools in Canada and USA: A Webometric perspective. Journal of Education for Library and Information Science, 43(2), 110-125.
  27. Thelwall, M. (2001). Results from a Web Impact Factor crawler, Journal of Documentation, 57(2), 177-191.
  28. Soualmia, L.F., Darmoni, S.J. Le Duff, F., Douyère, M., & Thelwall, M. (2002). Web Impact Factor: a bibliometric criterion applied to medical informatics societies’ Web sites, Medical Informatics in Europe MIE2002 congress (to be held in Budapest, Hungary, August 25-29).
  29. Douyère, M., Soualmia, L.F., Le Duff, F., Thelwall, M. & Darmoni, S.J. (2002). Web Impact Factor : un outil bibliométrique appliqué aux sites Web des facultés de médecine et des CHU français, Neuvièmes Journées Francophones d'Informatique Médicale. 6-7 mai 2002, Québec-Canada.
  30. Kousha, K. & Thelwall, M. (2005). Motivations for linking to open access LIS library and information science articles: Exploring characteristics of sources of Web citation. ISSI 2005.

Big Data Theory for Information Scientists

  1. Thelwall, M., Wouters, P., & Fry, J. (2008). Information-Centred Research for large-scale analysis of new information sources, Journal of the American Society for Information Science and Technology, 59(9), 1523-1527. [More about this theory and an explanation of its big data angle]
  2. Thelwall, M. & Wouters, P. (2005). What’s the deal with the web/Blogs/the next big technology: A key role for information science in e-social science research? CoLIS 2005, Lecture Notes in Computer Science 3507, 187-199.

Quantitative Methods for Social Science Research

  1. Thelwall, M. (2014). Sentiment analysis and time series with Twitter. In: Weller, K. Bruns, A. Burgess, J. Mahrt, M. Puschmann, C. (eds.) Twitter and Society. New York: Peter Lang (pp. 83-96).
  2. Thelwall, M. (2013). Introduction to webometrics and social web analysis [free in-progress draft copy]. University of Wolverhampton. [This is an updated and extended free ebook based upon the book "Introduction to Webometrics: Quantitative Web Research for the Social Sciences" below and extra chapters from a forthcoming book. It can be read on its own or as an update to the book below]
  3. Thelwall, M. (2011). Investigating human communication and language from traces left on the web. In: Malcolm Williams, W Paul Vogt, (Eds), The SAGE Handbook of Innovation in Social Research Methods, London: Sage. (pp. 167-181). [This includes some small link diagrams for Alan Turing]
  4. Wilkinson, D. & Thelwall, M. (2011). Researching personal information on the public Web: Methods and ethics, Social Science Computer Review, 29(4), 387-401. [please email for a copy].
  5. Thelwall, M. (2009). Introduction to Webometrics: Quantitative Web Research for the Social Sciences. San Rafael, CA: Morgan & Claypool (Synthesis Lectures on Information Concepts, Retrieval, and Services, 2009, Vol. 1, No. 1).
  6. Thelwall, M. (2004). Hyperlink analysis, Encyclopedia of Virtual communities and Technologies, Idea Group Inc.
  7. Park, H. & Thelwall, M. (2005). The network approach to web hyperlink research and its utility for science communication, In: Hine, C. (Ed.), Virtual Methods: Issues in Social Research on the Internet (chapter 13), London: Berg (pp. 171-181).
  8. Wilkinson, D., Thelwall, M. & Li, X. (2003). Exploiting hyperlinks to study academic Web use. Social Science Computer Review, 21(3), 340-351.
  9. Park, H. & Thelwall, M. (2003). Hyperlink analyses of the world wide web: A review. Journal of Computer-Mediated Communication. 8(4).

Web Text Analysis

  1. Shifman, L. & Thelwall, M. (2009). Assessing global diffusion with Web Memetics: The spread and evolution of a popular joke, Journal of the American Society for Information Science and Technology 60(12), 2567-2576.
  2. Thelwall, M. & Price, E. (2006). Language evolution and the spread of ideas: A procedure for identifying emergent hybrid word family members. Journal of the American Society for Information Science and Technology, 57(10), 1326-1337.
  3. Thelwall, M. (2005). Creating and using web corpora, International Journal of Corpus Linguistics, 10(4), 517-541.
  4. Price, E. & Thelwall, M. (2005). The clustering power of low frequency words in academic webs. Journal of the American Society for Information Science and Technology, 56(8), 883-888. [Cited in Nextgen Datacom Inc. patent: US 8316030 B2]
  5. Thelwall, M. (2005). Text characteristics of English language university web sites. Journal of the American Society for Information Science and Technology , 56(6), 609–619.

Scientific Web Intelligence

  1. Thelwall, M. (2005). Scientific Web Intelligence: Finding relationships in university webs. Communications of the ACM, 48(7), 93-96.
  2. Thelwall, M. (2004). Vocabulary Spectral Analysis as an exploratory tool for Scientific Web Intelligence. 8th International Conference on Information Visualisation (14-16 July 2004, London) In: Information Visualization (IV04), Los Alamitos, CA: IEEE, pp. 501-506.
  3. Thelwall, M. (2005). Scientific Web Intelligence. In: Wang, J. (Ed.) Encyclopedia of Data warehousing and mining, Idea Group Inc.

Web Issue Analysis

  1. Thelwall, M., Thelwall, S. & Fairclough, R. (2006). Automated web issue analysis: A nurse prescribing case study. Information Processing & Management (Informetrics special issue), 42(6), 1471-1483.
  2. Thelwall, M., Vann, K. & Fairclough, R. (2006). Web issue analysis: An Integrated Water Resource Management case study. Journal of the American Society for Information Science and Technology, 57(10), 1303-1314.

General Webometrics

  1. Levitt, J. & Thelwall, M. (2014). From Webometrics to altmetrics: One and a half decades of digital research at Wolverhampton. LIDA 2014.
  2. Thelwall, M., & Sud, P. (2012). Webometric research with the Bing Search API 2.0. Journal of Informetrics, 6(1), 44-52
  3. Thelwall, M. (2011). A comparison of link and URL citation counting. ASLIB Proceedings, 63(4), 419-425.
  4. Thelwall, M. (2010). Webometrics: emergent or doomed?, Information Research, 15(4) colis713.
  5. Thelwall, M. (2009). A webometric analysis of Olle Persson, In: Fredrik Åström, Rickard Danell, Birger Larsen and Jesper Wiborg Schneider (Eds), Celebrating scholarly communication studies: A festschrift for Olle Persson, ISSI (pp. 61-71).
  6. Thelwall, M. (2008). Bibliometrics to Webometrics, Journal of Information Science, 34(4), 605-621. [This paper has been accepted for publication in Journal of Information Science and the final (edited, revised and typeset), definitive version of this paper will be published in 2008 by SAGE Publications Ltd, All rights reserved. © SAGE Publications Ltd]
  7. Thelwall, M., & Ruschenburg, T. (2006). Webometrie, Information, Wissenschaft und Praxis, 57(8), 401-406.
  8. Thelwall, M., Vaughan, L. & Björneborn, L. (2005). Webometrics. In: Annual Review of Information Science and Technology 39, 81-135.
  9. Thelwall, M. & Stuart, D . (2006). Web crawling ethics revisited: Cost, privacy and denial of service. Journal of the American Society for Information Science and Technology, 57(13), 1771-1779.
  10. Thelwall, M. (2005). Webometrics. In: Drake, M. A. (Ed.) Encyclopedia of Library and Information Science, Second Edition, Marcel Dekker, Inc., New York, to appear online summer 2005.
  11. Kretschmer, H. & Thelwall, M. (2004). The way from librametry to webometrics. Journal of Information Management and Scientometrics, 1(1), 1-7.
  12. Thelwall, M. & Vaughan, L. (2004). Webometrics: An introduction to the special issue Journal of the American Society for Information Science and Technology . 55(14), 1213-1215.
  13. Thelwall, M. (2002). Research dissemination and invocation on the web. Online Information Review 26(6), 413-420.
  14. Thelwall, M. (2002). Methodologies for crawler based web surveys, Internet Research: Electronic Networking and Applications, 12(2), 124-138.

Search Engine Evaluation

  1. Wilkinson, D., & Thelwall, M. (2013). Search markets and search results: The case of Bing. Library and Information Science Research, 35(4), 318-325.10.1016/j.lisr.2013.04.006[(a) Webometric research can exploit search markets to get more search results, and (b) Bing results can vary substantially depending upon the location of the searcher.]
  2. Thelwall, M. & Sud, P. (2012). Webometric research with the Bing Search API 2.0. Journal of Informetrics, 6(1), 44-52.
  3. Thelwall, M. (2008). Quantitative comparisons of search engine results, Journal of the American Society for Information Science and Technology, 59(11), 1702-1710.
  4. Thelwall, M. (2008). Extracting accurate and complete results from search engines: Case study Windows Live. Journal of the American Society for Information Science and Technology, 59(1), 38-50. [The hit count estimates from search engines seem to estimate either (a) the total number of matches or (b) the number of matches after eliminating spam, same domain duplicates and near duplicates. This explains their variations in accuracy. This paper also introduces query splitting, an automatic variation of Judit Bar-Ilan's method to get extra matches for a query beyond those normally given by a search engine.]
  5. Vaughan, L. & Thelwall, M. (2004). Search engine coverage bias: evidence and possible causes, Information Processing & Management, 40(4), 693-707.
  6. Thelwall, M. & Vaughan, L. (2004). A fair history of the Web? Examining country balance in the Internet Archive, Library & Information Science Research, 26(2), 162-176.
  7. Thelwall, M. (2002). In praise of Google: finding law journal web sites, Online Information Review, 26(4), 271-272.
  8. Thelwall, M. (2002). Subject gateway sites and search engine ranking, Online Information Review, 26(2), 101-107. [Cited in Microsoft patent: US 7739281 B2]
  9. Thelwall, M. (2001). The responsiveness of search engine indexes, Cybermetrics, 5(1),
  10. Thelwall, M. (2000). Web Impact Factors and search engine coverage, Journal of Documentation, 56(2), 185-189.
  11. Thelwall, M. (2001). A survey of search engine capabilities useful in data mining, Proceedings of the ASIST Annual Meeting Volume 38 (ASIST 2001) 24-30.
  12. Thelwall, M. Binns, R. Harries, G. Page-Kennedy, T. Price E., & Wilkinson, D. (2001). Custom interfaces for advanced queries in search engines, ASLIB Proceedings, 53(10), 413-422. [Cited in Microsoft patent: US 7346613 B2]
  13. Thelwall, M. (2005). Directing students to new information types: A new role for Google in literature searches?, Internet Reference Services Quarterly , 10(3/4), 159-166.

Web Crawlers

  1. Thelwall, M. (2003). A free database of university Web links: Data collection issues. Cybermetrics, 6.
  2. Thelwall, M. (2001). A web crawler design for data mining, Journal of Information Science 27(5), 319-325. [Cited in Kabushiki Kaisha Square Enix, Tokyo patent: US 8321198 B2]

Link-Based Algorithms

  1. Rousseau, R. & Thelwall, M. (2004). Escher staircases on the world wide web. FirstMonday, 9(6).
  2. Musgrove, P., Binns, R., Page-Kennedy, T., & Thelwall, M. (2004). A method for identifying clusters in sets of interlinking Web spaces, Scientometrics, 58(3), 657-672.
  3. Thelwall, M. & Wilkinson, D. (2004). Finding similar academic Web sites with links, bibliometric couplings and colinks. Information Processing & Management, 40(3), 515-526.
  4. Thelwall, M. & Vaughan, L (2004). New versions of PageRank employing alternative Web document models, ASLIB Proceedings, 56(1), 24-33.
  5. Thelwall, M. (2003). Can Google’s PageRank be used to find the most important academic web pages? Journal of Documentation, 59(2), 205-217.
  6. Thelwall, M. (2003). A layered approach for investigating the topological structure of communities in the web, Journal of Documentation, 59(4), 410-429.

Online Social Marketing

  1. Cugelman, B., Thelwall, M. & Dawes, P. (2011). The psychology of online behavioural influence interventions: a meta-analysis. Journal of Medical Internet Research 13(1), e17.
  2. Cugelman, B., Thelwall, M., & Dawes, P. (2009). The dimensions of website credibility and their relation to active trust and behavioural impact, Communications of the Association for Information Systems, 24, 455-472.
  3. Cugelman, B., Thelwall M., & Dawes P. (2007). Can brotherhood be sold like soap…Online? An online social marketing and advocacy pilot study. Lecture Notes in Computer Science (Persuasive Technology 07), 4744, 144-147.

Commercial Web Sites

  1. Thelwall, M. (2001). Commercial web site links, Internet Research, 11(2), 114-124.
  2. Thelwall, M. (2000). Who is using the domain? Professional and media adoption of the Web, International Journal of Information Management, 20(6), 441-453
  3. Thelwall, M. (2000). Effective web sites for Small to Medium Sized Enterprises, Journal of Small Business and Enterprise Development, 7(2), 149-159.
  4. Thelwall, M. (2000). Commercial web sites: Lost in cyberspace?, Internet Research: Electronic Networking and Applications, 10(2), 150-159.
  5. Thelwall, M. (2000), Implications of Search Engine Coverage on the Viability of commercial websites, poster session in ICEIS 2000, 14/4/00, Staffordshire University.

Virtual Learning Environments

  1. Thelwall, M. (1999). Will MANs and SuperJANET dominate educational technology in the UK? International Journal of Educational Technology 1(1).
  2. Thelwall, M. (1998). A unique style of computer assisted assessment, Alt-J, 6(2) 49-57.
  3. Thelwall, M. (1998). A Virtual Reality Machine for Vector Geometry, Alt-C 98 poster/demonstration, University of Oxford.
  4. Thelwall, M. (1998). The Virtual Campus - Paradigm or Metaphor?, Alt-C 98, University of Oxford.
  5. Thompson, D., Thelwall, M. & McKenna, R. (1997). Developing a short course on the Internet for Business, Proceedings of the CTI computing conference, Dublin August 1997.

Computer Assisted Assessment

  1. Thelwall, M. (2001). Understanding and assessment methodology in an introductory statistics course, Journal of Computers in Mathematics and Science Teaching, 20(3), 251-264.
  2. Thelwall, M. (2000). Computer Based Assessment: A versatile educational tool. Journal of Computers and Education, 34, 37-49.
  3. Thelwall, M. (1999). Open Access Randomly Generated Tests: Assessment to Drive Learning, In Brown, S., Race, P. and Bull, J., Computer Assisted Assessment in Higher Education, London: Kogan Page. ISBN 0 7494 3035 4.
  4. Thelwall, M. (1999), The Promotion of Understanding by the use of Open Access Computerised Assessment in Introductory Mathematics and Statistics Courses, Alt-C 99, University of Bristol, September 1999.
  5. Thelwall, M. (1999), Randomly generated motivation from Maths and Stats Tests, Maths and Stats, 10(1), 13-16. (Magazine article)
  6. Bishop, P., Cox, B., Fothergill, R., Kyle, J., Lawson, D., Mitchell, M., Rathbone, J., Stone, E. and Thelwall, M. (2001), Inter-Institutional collaboration on easing the transition to university, LTSN Maths and Stats Newsletter, 1(1),5-8 .
  7. Thelwall, M. (1998). The Advantages of Randomly generated computer assisted assessment, Proceedings of the Computer Assisted Assessment Conference, Loughborough June 1998.

Operator Algebras (Pure Maths: Functional Analysis)

  1. Thelwall, M. (1991). Maximal triangular subalgebras of AF algebras, Journal of Operator Theory, 25(1), 163-176.
  2. Thelwall, M. (1991). Dilation theory for subalgebras of AF algebras, Journal of Operator Theory, 25(2), 275-282.
  3. Thelwall, M. (1989). Bimodule theory in the study of non-self-adjoint operator algebras. University of Lancaster.


  1. Gobron, S., Ahn, J., Paltoglou, G., Thelwall, M. & Thalmann, D. (2010). From sentence to emotion: A real-time three-dimensional graphics metaphor of emotions extracted from text. The Visual Computer: International Journal of Computer Graphics, 26(6-8), 505-519.
  2. Prabowo, R., Thelwall, M., Hellsten I., & Scharnhorst A., (2009). Evolving debate in online communication: A graph analytical approach, Internet Research.
  3. Thelwall, M. Barlow, A., & Vann, K. (2005). The limits of web-based empowerment: Integrated Water Resource Management case studies. First Monday 10(4).
  4. Thelwall, M. (2004). Will digital libraries generate a new need for multi-disciplinary research skills? LIBRES, 14(2).
  5. Thelwall, M. (2001). Web log file analysis: Backlinks and queries, ASLIB Proceedings, 53(6), 217-223. [Cited in Microsoft patent: US 8639773 B2]


  1. Culpeper, J., Archer, D., Findlay, A. & Thelwall, M. (2018). John Webster, the dark and violent playwright? ANQ: A Quarterly Journal of Short Articles Notes and Reviews, 31(3). 201-210. doi:10.1080/0895769X.2018.1445515
  2. Culpeper, J., Findlay, A., Cortese, B. & Thelwall, M. (2018). Measuring emotional temperatures in Shakespeare’s drama. English Text Construction, 11(1), 10-37.

Statistical Process Control

  1. Thompson, D., Homer, G. & Thelwall, M. (2000). An examination of the potential role of the Internet in distributed SPC and Quality Systems. Quality and Reliability Engineering International, 16(1), p51-57.
  2. Thelwall, M. (2000). Linking SPC data via the Internet, workshop at PCI 2000, Strathclyde University.
  3. Thompson, D., Homer, G. & Thelwall, M. (1999). SPC and Quality Systems: The Potential Role of the Internet, Proceedings of the 2nd International Conference on the Control of Industrial Processes, University of Newcastle, March 1999.