Search (14 results, page 1 of 1)

  • × year_i:[2010 TO 2020}
  • × author_ss:"Thelwall, M."
  1. Thelwall, M.; Sud, P.; Wilkinson, D.: Link and co-inlink network diagrams with URL citations or title mentions (2012) 0.08
    0.08046674 = product of:
      0.16093348 = sum of:
        0.16093348 = sum of:
          0.12607203 = weight(_text_:network in 57) [ClassicSimilarity], result of:
            0.12607203 = score(doc=57,freq=10.0), product of:
              0.22917621 = queryWeight, product of:
                4.4533744 = idf(docFreq=1398, maxDocs=44218)
                0.05146125 = queryNorm
              0.5501096 = fieldWeight in 57, product of:
                3.1622777 = tf(freq=10.0), with freq of:
                  10.0 = termFreq=10.0
                4.4533744 = idf(docFreq=1398, maxDocs=44218)
                0.0390625 = fieldNorm(doc=57)
          0.034861445 = weight(_text_:22 in 57) [ClassicSimilarity], result of:
            0.034861445 = score(doc=57,freq=2.0), product of:
              0.18020853 = queryWeight, product of:
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.05146125 = queryNorm
              0.19345059 = fieldWeight in 57, product of:
                1.4142135 = tf(freq=2.0), with freq of:
                  2.0 = termFreq=2.0
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.0390625 = fieldNorm(doc=57)
      0.5 = coord(1/2)
    
    Abstract
    Webometric network analyses have been used to map the connectivity of groups of websites to identify clusters, important sites or overall structure. Such analyses have mainly been based upon hyperlink counts, the number of hyperlinks between a pair of websites, although some have used title mentions or URL citations instead. The ability to automatically gather hyperlink counts from Yahoo! ceased in April 2011 and the ability to manually gather such counts was due to cease by early 2012, creating a need for alternatives. This article assesses URL citations and title mentions as possible replacements for hyperlinks in both binary and weighted direct link and co-inlink network diagrams. It also assesses three different types of data for the network connections: hit count estimates, counts of matching URLs, and filtered counts of matching URLs. Results from analyses of U.S. library and information science departments and U.K. universities give evidence that metrics based upon URLs or titles can be appropriate replacements for metrics based upon hyperlinks for both binary and weighted networks, although filtered counts of matching URLs are necessary to give the best results for co-title mention and co-URL citation network diagrams.
    Date
    6. 4.2012 18:16:22
  2. Li, X.; Thelwall, M.; Kousha, K.: ¬The role of arXiv, RePEc, SSRN and PMC in formal scholarly communication (2015) 0.06
    0.057298202 = product of:
      0.114596404 = sum of:
        0.114596404 = sum of:
          0.07973496 = weight(_text_:network in 2593) [ClassicSimilarity], result of:
            0.07973496 = score(doc=2593,freq=4.0), product of:
              0.22917621 = queryWeight, product of:
                4.4533744 = idf(docFreq=1398, maxDocs=44218)
                0.05146125 = queryNorm
              0.34791988 = fieldWeight in 2593, product of:
                2.0 = tf(freq=4.0), with freq of:
                  4.0 = termFreq=4.0
                4.4533744 = idf(docFreq=1398, maxDocs=44218)
                0.0390625 = fieldNorm(doc=2593)
          0.034861445 = weight(_text_:22 in 2593) [ClassicSimilarity], result of:
            0.034861445 = score(doc=2593,freq=2.0), product of:
              0.18020853 = queryWeight, product of:
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.05146125 = queryNorm
              0.19345059 = fieldWeight in 2593, product of:
                1.4142135 = tf(freq=2.0), with freq of:
                  2.0 = termFreq=2.0
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.0390625 = fieldNorm(doc=2593)
      0.5 = coord(1/2)
    
    Abstract
    Purpose The four major Subject Repositories (SRs), arXiv, Research Papers in Economics (RePEc), Social Science Research Network (SSRN) and PubMed Central (PMC), are all important within their disciplines but no previous study has systematically compared how often they are cited in academic publications. In response, the purpose of this paper is to report an analysis of citations to SRs from Scopus publications, 2000-2013. Design/methodology/approach Scopus searches were used to count the number of documents citing the four SRs in each year. A random sample of 384 documents citing the four SRs was then visited to investigate the nature of the citations. Findings Each SR was most cited within its own subject area but attracted substantial citations from other subject areas, suggesting that they are open to interdisciplinary uses. The proportion of documents citing each SR is continuing to increase rapidly, and the SRs all seem to attract substantial numbers of citations from more than one discipline. Research limitations/implications Scopus does not cover all publications, and most citations to documents found in the four SRs presumably cite the published version, when one exists, rather than the repository version. Practical implications SRs are continuing to grow and do not seem to be threatened by institutional repositories and so research managers should encourage their continued use within their core disciplines, including for research that aims at an audience in other disciplines. Originality/value This is the first simultaneous analysis of Scopus citations to the four most popular SRs.
    Date
    20. 1.2015 18:30:22
    Object
    Social Science Research Network
  3. Thelwall, M.; Buckley, K.; Paltoglou, G.; Cai, D.; Kappas, A.: Sentiment strength detection in short informal text (2010) 0.05
    0.045621287 = product of:
      0.091242574 = sum of:
        0.091242574 = sum of:
          0.05638113 = weight(_text_:network in 4200) [ClassicSimilarity], result of:
            0.05638113 = score(doc=4200,freq=2.0), product of:
              0.22917621 = queryWeight, product of:
                4.4533744 = idf(docFreq=1398, maxDocs=44218)
                0.05146125 = queryNorm
              0.2460165 = fieldWeight in 4200, product of:
                1.4142135 = tf(freq=2.0), with freq of:
                  2.0 = termFreq=2.0
                4.4533744 = idf(docFreq=1398, maxDocs=44218)
                0.0390625 = fieldNorm(doc=4200)
          0.034861445 = weight(_text_:22 in 4200) [ClassicSimilarity], result of:
            0.034861445 = score(doc=4200,freq=2.0), product of:
              0.18020853 = queryWeight, product of:
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.05146125 = queryNorm
              0.19345059 = fieldWeight in 4200, product of:
                1.4142135 = tf(freq=2.0), with freq of:
                  2.0 = termFreq=2.0
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.0390625 = fieldNorm(doc=4200)
      0.5 = coord(1/2)
    
    Abstract
    A huge number of informal messages are posted every day in social network sites, blogs, and discussion forums. Emotions seem to be frequently important in these texts for expressing friendship, showing social support or as part of online arguments. Algorithms to identify sentiment and sentiment strength are needed to help understand the role of emotion in this informal communication and also to identify inappropriate or anomalous affective utterances, potentially associated with threatening behavior to the self or others. Nevertheless, existing sentiment detection algorithms tend to be commercially oriented, designed to identify opinions about products rather than user behaviors. This article partly fills this gap with a new algorithm, SentiStrength, to extract sentiment strength from informal English text, using new methods to exploit the de facto grammars and spelling styles of cyberspace. Applied to MySpace comments and with a lookup table of term sentiment strengths optimized by machine learning, SentiStrength is able to predict positive emotion with 60.6% accuracy and negative emotion with 72.8% accuracy, both based upon strength scales of 1-5. The former, but not the latter, is better than baseline and a wide range of general machine learning approaches.
    Date
    22. 1.2011 14:29:23
  4. Thelwall, M.; Kousha, K.: Academia.edu : Social network or Academic Network? (2014) 0.02
    0.024413744 = product of:
      0.048827488 = sum of:
        0.048827488 = product of:
          0.097654976 = sum of:
            0.097654976 = weight(_text_:network in 1234) [ClassicSimilarity], result of:
              0.097654976 = score(doc=1234,freq=6.0), product of:
                0.22917621 = queryWeight, product of:
                  4.4533744 = idf(docFreq=1398, maxDocs=44218)
                  0.05146125 = queryNorm
                0.42611307 = fieldWeight in 1234, product of:
                  2.4494898 = tf(freq=6.0), with freq of:
                    6.0 = termFreq=6.0
                  4.4533744 = idf(docFreq=1398, maxDocs=44218)
                  0.0390625 = fieldNorm(doc=1234)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    Abstract
    Academic social network sites Academia.edu and ResearchGate, and reference sharing sites Mendeley, Bibsonomy, Zotero, and CiteULike, give scholars the ability to publicize their research outputs and connect with each other. With millions of users, these are a significant addition to the scholarly communication and academic information-seeking eco-structure. There is thus a need to understand the role that they play and the changes, if any, that they can make to the dynamics of academic careers. This article investigates attributes of philosophy scholars on Academia.edu, introducing a median-based, time-normalizing method to adjust for time delays in joining the site. In comparison to students, faculty tend to attract more profile views but female philosophers did not attract more profile views than did males, suggesting that academic capital drives philosophy uses of the site more than does friendship and networking. Secondary analyses of law, history, and computer science confirmed the faculty advantage (in terms of higher profile views) except for females in law and females in computer science. There was also a female advantage for both faculty and students in law and computer science as well as for history students. Hence, Academia.edu overall seems to reflect a hybrid of scholarly norms (the faculty advantage) and a female advantage that is suggestive of general social networking norms. Finally, traditional bibliometric measures did not correlate with any Academia.edu metrics for philosophers, perhaps because more senior academics use the site less extensively or because of the range informal scholarly activities that cannot be measured by bibliometric methods.
  5. Thelwall, M.; Wilkinson, D.: Public dialogs in social network sites : What is their purpose? (2010) 0.02
    0.023920486 = product of:
      0.04784097 = sum of:
        0.04784097 = product of:
          0.09568194 = sum of:
            0.09568194 = weight(_text_:network in 3327) [ClassicSimilarity], result of:
              0.09568194 = score(doc=3327,freq=4.0), product of:
                0.22917621 = queryWeight, product of:
                  4.4533744 = idf(docFreq=1398, maxDocs=44218)
                  0.05146125 = queryNorm
                0.41750383 = fieldWeight in 3327, product of:
                  2.0 = tf(freq=4.0), with freq of:
                    4.0 = termFreq=4.0
                  4.4533744 = idf(docFreq=1398, maxDocs=44218)
                  0.046875 = fieldNorm(doc=3327)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    Abstract
    Social network sites (SNSs) such as MySpace and Facebook are important venues for interpersonal communication, especially among youth. One way in which members can communicate is to write public messages on each other's profile, but how is this unusual means of communication used in practice? An analysis of 2,293 public comment exchanges extracted from large samples of U.S. and U.K. MySpace members found them to be relatively rapid, but rarely used for prolonged exchanges. They seem to fulfill two purposes: making initial contact and keeping in touch occasionally such as at birthdays and other important dates. Although about half of the dialogs seem to exchange some gossip, the dialogs seem typically too short to play the role of gossip-based social grooming for typical pairs of Friends, but close Friends may still communicate extensively in SNSs with other methods.
  6. Wilkinson, D.; Thelwall, M.: Social network site changes over time : the case of MySpace (2010) 0.02
    0.01993374 = product of:
      0.03986748 = sum of:
        0.03986748 = product of:
          0.07973496 = sum of:
            0.07973496 = weight(_text_:network in 4106) [ClassicSimilarity], result of:
              0.07973496 = score(doc=4106,freq=4.0), product of:
                0.22917621 = queryWeight, product of:
                  4.4533744 = idf(docFreq=1398, maxDocs=44218)
                  0.05146125 = queryNorm
                0.34791988 = fieldWeight in 4106, product of:
                  2.0 = tf(freq=4.0), with freq of:
                    4.0 = termFreq=4.0
                  4.4533744 = idf(docFreq=1398, maxDocs=44218)
                  0.0390625 = fieldNorm(doc=4106)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    Abstract
    The uptake of social network sites (SNSs) has been highly trend-driven, with Friendster, MySpace, and Facebook being successively the most popular. Given that teens are often early adopters of communication technologies, it seems reasonable to assume that the typical user of any particular SNS would change over time, probably becoming older and covering different segments of the population. This article analyzes changes in MySpace self-reported member demographics and behavior from 2007 to 2010 using four large samples of members and focusing on the United States. The results indicate that despite its take-up rate declining, with only about 1 in 10 members being active a year after joining, the dominant (modal) age for active U.S. members remains midadolescence, but has shifted by about 2 years from 15 to 17, and the U.S. dominance of MySpace is shrinking. There also has been a dramatic increase in the median number of Friends for new U.S. members, from 12 to 96-probably due to MySpace's automated Friend Finder. Some factors show little change, however, including the female majority, the 5% minority gay membership, and the approximately 50% private profiles. In addition, there has been an increase in the proportion of Latino/Hispanic U.S. members, suggesting a shifting ethnic profile. Overall, MySpace has surprisingly stable membership demographics and is apparently maintaining its primary youth appeal, perhaps because of its music orientation.
  7. Thelwall, M.; Kousha, K.: Goodreads : a social network site for book readers (2017) 0.02
    0.01993374 = product of:
      0.03986748 = sum of:
        0.03986748 = product of:
          0.07973496 = sum of:
            0.07973496 = weight(_text_:network in 3534) [ClassicSimilarity], result of:
              0.07973496 = score(doc=3534,freq=4.0), product of:
                0.22917621 = queryWeight, product of:
                  4.4533744 = idf(docFreq=1398, maxDocs=44218)
                  0.05146125 = queryNorm
                0.34791988 = fieldWeight in 3534, product of:
                  2.0 = tf(freq=4.0), with freq of:
                    4.0 = termFreq=4.0
                  4.4533744 = idf(docFreq=1398, maxDocs=44218)
                  0.0390625 = fieldNorm(doc=3534)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    Abstract
    Goodreads is an Amazon-owned book-based social web site for members to share books, read, review books, rate books, and connect with other readers. Goodreads has tens of millions of book reviews, recommendations, and ratings that may help librarians and readers to select relevant books. This article describes a first investigation of the properties of Goodreads users, using a random sample of 50,000 members. The results suggest that about three quarters of members with a public profile are female, and that there is little difference between male and female users in patterns of behavior, except for females registering more books and rating them less positively. Goodreads librarians and super-users engage extensively with most features of the site. The absence of strong correlations between book-based and social usage statistics (e.g., numbers of friends, followers, books, reviews, and ratings) suggests that members choose their own individual balance of social and book activities and rarely ignore one at the expense of the other. Goodreads is therefore neither primarily a book-based website nor primarily a social network site but is a genuine hybrid, social navigation site.
  8. Thelwall, M.; Kousha, K.: ResearchGate: Disseminating, communicating, and measuring scholarship? (2015) 0.02
    0.016914338 = product of:
      0.033828676 = sum of:
        0.033828676 = product of:
          0.06765735 = sum of:
            0.06765735 = weight(_text_:network in 1813) [ClassicSimilarity], result of:
              0.06765735 = score(doc=1813,freq=2.0), product of:
                0.22917621 = queryWeight, product of:
                  4.4533744 = idf(docFreq=1398, maxDocs=44218)
                  0.05146125 = queryNorm
                0.29521978 = fieldWeight in 1813, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  4.4533744 = idf(docFreq=1398, maxDocs=44218)
                  0.046875 = fieldNorm(doc=1813)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    Abstract
    ResearchGate is a social network site for academics to create their own profiles, list their publications, and interact with each other. Like Academia.edu, it provides a new way for scholars to disseminate their work and hence potentially changes the dynamics of informal scholarly communication. This article assesses whether ResearchGate usage and publication data broadly reflect existing academic hierarchies and whether individual countries are set to benefit or lose out from the site. The results show that rankings based on ResearchGate statistics correlate moderately well with other rankings of academic institutions, suggesting that ResearchGate use broadly reflects the traditional distribution of academic capital. Moreover, while Brazil, India, and some other countries seem to be disproportionately taking advantage of ResearchGate, academics in China, South Korea, and Russia may be missing opportunities to use ResearchGate to maximize the academic impact of their publications.
  9. Thelwall, M.; Bourrier, M.K.: ¬The reading background of Goodreads book club members : a female fiction canon? (2019) 0.01
    0.014095282 = product of:
      0.028190564 = sum of:
        0.028190564 = product of:
          0.05638113 = sum of:
            0.05638113 = weight(_text_:network in 5461) [ClassicSimilarity], result of:
              0.05638113 = score(doc=5461,freq=2.0), product of:
                0.22917621 = queryWeight, product of:
                  4.4533744 = idf(docFreq=1398, maxDocs=44218)
                  0.05146125 = queryNorm
                0.2460165 = fieldWeight in 5461, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  4.4533744 = idf(docFreq=1398, maxDocs=44218)
                  0.0390625 = fieldNorm(doc=5461)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    Abstract
    Purpose Despite the social, educational and therapeutic benefits of book clubs, little is known about which books participants are likely to have read. In response, the purpose of this paper is to investigate the public bookshelves of those that have joined a group within the Goodreads social network site. Design/methodology/approach Books listed as read by members of 50 large English-language Goodreads groups - with a genre focus or other theme - were compiled by author and title. Findings Recent and youth-oriented fiction dominate the 50 books most read by book club members, whilst almost half are works of literature frequently taught at the secondary and postsecondary level (literary classics). Whilst J.K. Rowling is almost ubiquitous (at least 63 per cent as frequently listed as other authors in any group, including groups for other genres), most authors, including Shakespeare (15 per cent), Goulding (6 per cent) and Hemmingway (9 per cent), are little read by some groups. Nor are individual recent literary prize winners or works in languages other than English frequently read. Research limitations/implications Although these results are derived from a single popular website, knowing more about what book club members are likely to have read should help participants, organisers and moderators. For example, recent literary prize winners might be a good choice, given that few members may have read them. Originality/value This is the first large scale study of book group members' reading patterns. Whilst typical reading is likely to vary by group theme and average age, there seems to be a mainly female canon of about 14 authors and 19 books that Goodreads book club members are likely to have read.
  10. Thelwall, M.; Buckley, K.; Paltoglou, G.: Sentiment in Twitter events (2011) 0.01
    0.010458433 = product of:
      0.020916866 = sum of:
        0.020916866 = product of:
          0.041833732 = sum of:
            0.041833732 = weight(_text_:22 in 4345) [ClassicSimilarity], result of:
              0.041833732 = score(doc=4345,freq=2.0), product of:
                0.18020853 = queryWeight, product of:
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.05146125 = queryNorm
                0.23214069 = fieldWeight in 4345, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.046875 = fieldNorm(doc=4345)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    Date
    22. 1.2011 14:27:06
  11. Thelwall, M.; Maflahi, N.: Guideline references and academic citations as evidence of the clinical value of health research (2016) 0.01
    0.010458433 = product of:
      0.020916866 = sum of:
        0.020916866 = product of:
          0.041833732 = sum of:
            0.041833732 = weight(_text_:22 in 2856) [ClassicSimilarity], result of:
              0.041833732 = score(doc=2856,freq=2.0), product of:
                0.18020853 = queryWeight, product of:
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.05146125 = queryNorm
                0.23214069 = fieldWeight in 2856, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.046875 = fieldNorm(doc=2856)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    Date
    19. 3.2016 12:22:00
  12. Thelwall, M.; Sud, P.: Mendeley readership counts : an investigation of temporal and disciplinary differences (2016) 0.01
    0.010458433 = product of:
      0.020916866 = sum of:
        0.020916866 = product of:
          0.041833732 = sum of:
            0.041833732 = weight(_text_:22 in 3211) [ClassicSimilarity], result of:
              0.041833732 = score(doc=3211,freq=2.0), product of:
                0.18020853 = queryWeight, product of:
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.05146125 = queryNorm
                0.23214069 = fieldWeight in 3211, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.046875 = fieldNorm(doc=3211)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    Date
    16.11.2016 11:07:22
  13. Didegah, F.; Thelwall, M.: Co-saved, co-tweeted, and co-cited networks (2018) 0.01
    0.010458433 = product of:
      0.020916866 = sum of:
        0.020916866 = product of:
          0.041833732 = sum of:
            0.041833732 = weight(_text_:22 in 4291) [ClassicSimilarity], result of:
              0.041833732 = score(doc=4291,freq=2.0), product of:
                0.18020853 = queryWeight, product of:
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.05146125 = queryNorm
                0.23214069 = fieldWeight in 4291, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.046875 = fieldNorm(doc=4291)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    Date
    28. 7.2018 10:00:22
  14. Thelwall, M.: Are Mendeley reader counts high enough for research evaluations when articles are published? (2017) 0.01
    0.008715361 = product of:
      0.017430723 = sum of:
        0.017430723 = product of:
          0.034861445 = sum of:
            0.034861445 = weight(_text_:22 in 3806) [ClassicSimilarity], result of:
              0.034861445 = score(doc=3806,freq=2.0), product of:
                0.18020853 = queryWeight, product of:
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.05146125 = queryNorm
                0.19345059 = fieldWeight in 3806, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.0390625 = fieldNorm(doc=3806)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    Date
    20. 1.2015 18:30:22