Search (2 results, page 1 of 1)

  • × author_ss:"Thelwall, M."
  • × year_i:[2020 TO 2030}
  1. Thelwall, M.; Kousha, K.; Abdoli, M.; Stuart, E.; Makita, M.; Wilson, P.; Levitt, J.: Why are coauthored academic articles more cited : higher quality or larger audience? (2023) 0.00
    0.0020646858 = product of:
      0.018582173 = sum of:
        0.0136091355 = product of:
          0.027218271 = sum of:
            0.027218271 = weight(_text_:networks in 995) [ClassicSimilarity], result of:
              0.027218271 = score(doc=995,freq=2.0), product of:
                0.10416738 = queryWeight, product of:
                  4.72992 = idf(docFreq=1060, maxDocs=44218)
                  0.022023074 = queryNorm
                0.26129362 = fieldWeight in 995, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  4.72992 = idf(docFreq=1060, maxDocs=44218)
                  0.0390625 = fieldNorm(doc=995)
          0.5 = coord(1/2)
        0.004973038 = product of:
          0.014919113 = sum of:
            0.014919113 = weight(_text_:22 in 995) [ClassicSimilarity], result of:
              0.014919113 = score(doc=995,freq=2.0), product of:
                0.07712106 = queryWeight, product of:
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.022023074 = queryNorm
                0.19345059 = fieldWeight in 995, 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=995)
          0.33333334 = coord(1/3)
      0.11111111 = coord(2/18)
    
    Abstract
    Collaboration is encouraged because it is believed to improve academic research, supported by indirect evidence in the form of more coauthored articles being more cited. Nevertheless, this might not reflect quality but increased self-citations or the "audience effect": citations from increased awareness through multiple author networks. We address this with the first science wide investigation into whether author numbers associate with journal article quality, using expert peer quality judgments for 122,331 articles from the 2014-20 UK national assessment. Spearman correlations between author numbers and quality scores show moderately strong positive associations (0.2-0.4) in the health, life, and physical sciences, but weak or no positive associations in engineering and social sciences, with weak negative/positive or no associations in various arts and humanities, and a possible negative association for decision sciences. This gives the first systematic evidence that greater numbers of authors associates with higher quality journal articles in the majority of academia outside the arts and humanities, at least for the UK. Positive associations between team size and citation counts in areas with little association between team size and quality also show that audience effects or other nonquality factors account for the higher citation rates of coauthored articles in some fields.
    Date
    22. 6.2023 18:11:50
  2. Thelwall, M.; Thelwall, S.: ¬A thematic analysis of highly retweeted early COVID-19 tweets : consensus, information, dissent and lockdown life (2020) 0.00
    0.0013889229 = product of:
      0.012500307 = sum of:
        0.007527269 = product of:
          0.015054538 = sum of:
            0.015054538 = weight(_text_:29 in 178) [ClassicSimilarity], result of:
              0.015054538 = score(doc=178,freq=2.0), product of:
                0.07747029 = queryWeight, product of:
                  3.5176873 = idf(docFreq=3565, maxDocs=44218)
                  0.022023074 = queryNorm
                0.19432661 = fieldWeight in 178, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  3.5176873 = idf(docFreq=3565, maxDocs=44218)
                  0.0390625 = fieldNorm(doc=178)
          0.5 = coord(1/2)
        0.004973038 = product of:
          0.014919113 = sum of:
            0.014919113 = weight(_text_:22 in 178) [ClassicSimilarity], result of:
              0.014919113 = score(doc=178,freq=2.0), product of:
                0.07712106 = queryWeight, product of:
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.022023074 = queryNorm
                0.19345059 = fieldWeight in 178, 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=178)
          0.33333334 = coord(1/3)
      0.11111111 = coord(2/18)
    
    Abstract
    Purpose Public attitudes towards COVID-19 and social distancing are critical in reducing its spread. It is therefore important to understand public reactions and information dissemination in all major forms, including on social media. This article investigates important issues reflected on Twitter in the early stages of the public reaction to COVID-19. Design/methodology/approach A thematic analysis of the most retweeted English-language tweets mentioning COVID-19 during March 10-29, 2020. Findings The main themes identified for the 87 qualifying tweets accounting for 14 million retweets were: lockdown life; attitude towards social restrictions; politics; safety messages; people with COVID-19; support for key workers; work; and COVID-19 facts/news. Research limitations/implications Twitter played many positive roles, mainly through unofficial tweets. Users shared social distancing information, helped build support for social distancing, criticised government responses, expressed support for key workers and helped each other cope with social isolation. A few popular tweets not supporting social distancing show that government messages sometimes failed. Practical implications Public health campaigns in future may consider encouraging grass roots social web activity to support campaign goals. At a methodological level, analysing retweet counts emphasised politics and ignored practical implementation issues. Originality/value This is the first qualitative analysis of general COVID-19-related retweeting.
    Date
    20. 1.2015 18:30:22