Search (13 results, page 1 of 1)

  • × year_i:[2010 TO 2020}
  • × author_ss:"Thelwall, M."
  1. Wilkinson, D.; Thelwall, M.: Trending Twitter topics in English : an international comparison (2012) 0.05 
    0.047354616 = product of:
  0.09470923 = sum of:
    0.09470923 = product of:
      0.18941846 = sum of:
        0.18941846 = weight(_text_:news in 375) [ClassicSimilarity], result of:
          0.18941846 = score(doc=375,freq=12.0), product of:
            0.26705483 = queryWeight, product of:
              5.2416887 = idf(docFreq=635, maxDocs=44218)
              0.05094824 = queryNorm
            0.7092868 = fieldWeight in 375, product of:
              3.4641016 = tf(freq=12.0), with freq of:
                12.0 = termFreq=12.0
              5.2416887 = idf(docFreq=635, maxDocs=44218)
              0.0390625 = fieldNorm(doc=375)
      0.5 = coord(1/2)
  0.5 = coord(1/2)
    Abstract
    The worldwide span of the microblogging service Twitter provides an opportunity to make international comparisons of trending topics of interest, such as news stories. Previous international comparisons of news interests have tended to use surveys and may bypass topics not well covered in the mainstream media. This study uses 9 months of English-language Tweets from the United Kingdom, United States, India, South Africa, New Zealand, and Australia. Based upon the top 50 trending keywords in each country from the 0.5 billion Tweets collected, festivals or religious events are the most common, followed by media events, politics, human interest, and sports. U.S. trending topics have the most interest in the other countries and Indian trending topics the least. Conversely, India is the most interested in other countries' trending topics and the United States the least. This gives evidence of an international hierarchy of perceived importance or relevance with some issues, such as the international interest in U.S. Thanksgiving celebrations, apparently not being directly driven by the media. This hierarchy echoes, and may be caused by, similar news coverage trends. Although the current imbalanced international news coverage does not seem to be out of step with public news interests, the political implication is that the Twitter-using public reflects, and hence seems to implicitly accept, international imbalances in news media agenda setting rather than combating them. This is an issue for those believing that these imbalances make the media too powerful.
  2. Kousha, K.; Thelwall, M.: News stories as evidence for research? : BBC citations from articles, Books, and Wikipedia (2017) 0.05 
    0.047354616 = product of:
  0.09470923 = sum of:
    0.09470923 = product of:
      0.18941846 = sum of:
        0.18941846 = weight(_text_:news in 3760) [ClassicSimilarity], result of:
          0.18941846 = score(doc=3760,freq=12.0), product of:
            0.26705483 = queryWeight, product of:
              5.2416887 = idf(docFreq=635, maxDocs=44218)
              0.05094824 = queryNorm
            0.7092868 = fieldWeight in 3760, product of:
              3.4641016 = tf(freq=12.0), with freq of:
                12.0 = termFreq=12.0
              5.2416887 = idf(docFreq=635, maxDocs=44218)
              0.0390625 = fieldNorm(doc=3760)
      0.5 = coord(1/2)
  0.5 = coord(1/2)
    Abstract
    Although news stories target the general public and are sometimes inaccurate, they can serve as sources of real-world information for researchers. This article investigates the extent to which academics exploit journalism using content and citation analyses of online BBC News stories cited by Scopus articles. A total of 27,234 Scopus-indexed publications have cited at least one BBC News story, with a steady annual increase. Citations from the arts and humanities (2.8% of publications in 2015) and social sciences (1.5%) were more likely than citations from medicine (0.1%) and science (<0.1%). Surprisingly, half of the sampled Scopus-cited science and technology (53%) and medicine and health (47%) stories were based on academic research, rather than otherwise unpublished information, suggesting that researchers have chosen a lower-quality secondary source for their citations. Nevertheless, the BBC News stories that were most frequently cited by Scopus, Google Books, and Wikipedia introduced new information from many different topics, including politics, business, economics, statistics, and reports about events. Thus, news stories are mediating real-world knowledge into the academic domain, a potential cause for concern.
  3. Thelwall, M.; Buckley, K.; Paltoglou, G.: Sentiment strength detection for the social web (2012) 0.02 
    0.01933244 = product of:
  0.03866488 = sum of:
    0.03866488 = product of:
      0.07732976 = sum of:
        0.07732976 = weight(_text_:news in 4972) [ClassicSimilarity], result of:
          0.07732976 = score(doc=4972,freq=2.0), product of:
            0.26705483 = queryWeight, product of:
              5.2416887 = idf(docFreq=635, maxDocs=44218)
              0.05094824 = queryNorm
            0.28956512 = fieldWeight in 4972, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              5.2416887 = idf(docFreq=635, maxDocs=44218)
              0.0390625 = fieldNorm(doc=4972)
      0.5 = coord(1/2)
  0.5 = coord(1/2)
    Abstract
    Sentiment analysis is concerned with the automatic extraction of sentiment-related information from text. Although most sentiment analysis addresses commercial tasks, such as extracting opinions from product reviews, there is increasing interest in the affective dimension of the social web, and Twitter in particular. Most sentiment analysis algorithms are not ideally suited to this task because they exploit indirect indicators of sentiment that can reflect genre or topic instead. Hence, such algorithms used to process social web texts can identify spurious sentiment patterns caused by topics rather than affective phenomena. This article assesses an improved version of the algorithm SentiStrength for sentiment strength detection across the social web that primarily uses direct indications of sentiment. The results from six diverse social web data sets (MySpace, Twitter, YouTube, Digg, Runners World, BBC Forums) indicate that SentiStrength 2 is successful in the sense of performing better than a baseline approach for all data sets in both supervised and unsupervised cases. SentiStrength is not always better than machine-learning approaches that exploit indirect indicators of sentiment, however, and is particularly weaker for positive sentiment in news-related discussions. Overall, the results suggest that, even unsupervised, SentiStrength is robust enough to be applied to a wide variety of different social web contexts.
  4. Kousha, K.; Thelwall, M.; Abdoli, M.: ¬The role of online videos in research communication : a content analysis of YouTube videos cited in academic publications (2012) 0.02 
    0.01933244 = product of:
  0.03866488 = sum of:
    0.03866488 = product of:
      0.07732976 = sum of:
        0.07732976 = weight(_text_:news in 382) [ClassicSimilarity], result of:
          0.07732976 = score(doc=382,freq=2.0), product of:
            0.26705483 = queryWeight, product of:
              5.2416887 = idf(docFreq=635, maxDocs=44218)
              0.05094824 = queryNorm
            0.28956512 = fieldWeight in 382, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              5.2416887 = idf(docFreq=635, maxDocs=44218)
              0.0390625 = fieldNorm(doc=382)
      0.5 = coord(1/2)
  0.5 = coord(1/2)
    Abstract
    Although there is some evidence that online videos are increasingly used by academics for informal scholarly communication and teaching, the extent to which they are used in published academic research is unknown. This article explores the extent to which YouTube videos are cited in academic publications and whether there are significant broad disciplinary differences in this practice. To investigate, we extracted the URL citations to YouTube videos from academic publications indexed by Scopus. A total of 1,808 Scopus publications cited at least one YouTube video, and there was a steady upward growth in citing online videos within scholarly publications from 2006 to 2011, with YouTube citations being most common within arts and humanities (0.3%) and the social sciences (0.2%). A content analysis of 551 YouTube videos cited by research articles indicated that in science (78%) and in medicine and health sciences (77%), over three fourths of the cited videos had either direct scientific (e.g., laboratory experiments) or scientific-related contents (e.g., academic lectures or education) whereas in the arts and humanities, about 80% of the YouTube videos had art, culture, or history themes, and in the social sciences, about 63% of the videos were related to news, politics, advertisements, and documentaries. This shows both the disciplinary differences and the wide variety of innovative research communication uses found for videos within the different subject areas.
  5. Thelwall, M.; Goriunova, O.; Vis, F.; Faulkner, S.; Burns, A.; Aulich, J.; Mas-Bleda, A.; Stuart, E.; D'Orazio, F.: Chatting through pictures : a classification of images tweeted in one week in the UK and USA (2016) 0.02 
    0.01933244 = product of:
  0.03866488 = sum of:
    0.03866488 = product of:
      0.07732976 = sum of:
        0.07732976 = weight(_text_:news in 3215) [ClassicSimilarity], result of:
          0.07732976 = score(doc=3215,freq=2.0), product of:
            0.26705483 = queryWeight, product of:
              5.2416887 = idf(docFreq=635, maxDocs=44218)
              0.05094824 = queryNorm
            0.28956512 = fieldWeight in 3215, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              5.2416887 = idf(docFreq=635, maxDocs=44218)
              0.0390625 = fieldNorm(doc=3215)
      0.5 = coord(1/2)
  0.5 = coord(1/2)
    Abstract
    Twitter is used by a substantial minority of the populations of many countries to share short messages, sometimes including images. Nevertheless, despite some research into specific images, such as selfies, and a few news stories about specific tweeted photographs, little is known about the types of images that are routinely shared. In response, this article reports a content analysis of random samples of 800 images tweeted from the UK or USA during a week at the end of 2014. Although most images were photographs, a substantial minority were hybrid or layered image forms: phone screenshots, collages, captioned pictures, and pictures of text messages. About half were primarily of one or more people, including 10% that were selfies, but a wide variety of other things were also pictured. Some of the images were for advertising or to share a joke but in most cases the purpose of the tweet seemed to be to share the minutiae of daily lives, performing the function of chat or gossip, sometimes in innovative ways.
  6. Thelwall, M.; Buckley, K.; Paltoglou, G.: Sentiment in Twitter events (2011) 0.01 
    0.010354174 = product of:
  0.020708349 = sum of:
    0.020708349 = product of:
      0.041416697 = sum of:
        0.041416697 = weight(_text_:22 in 4345) [ClassicSimilarity], result of:
          0.041416697 = score(doc=4345,freq=2.0), product of:
            0.17841205 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.05094824 = 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
  7. Thelwall, M.; Maflahi, N.: Guideline references and academic citations as evidence of the clinical value of health research (2016) 0.01 
    0.010354174 = product of:
  0.020708349 = sum of:
    0.020708349 = product of:
      0.041416697 = sum of:
        0.041416697 = weight(_text_:22 in 2856) [ClassicSimilarity], result of:
          0.041416697 = score(doc=2856,freq=2.0), product of:
            0.17841205 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.05094824 = 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
  8. Thelwall, M.; Sud, P.: Mendeley readership counts : an investigation of temporal and disciplinary differences (2016) 0.01 
    0.010354174 = product of:
  0.020708349 = sum of:
    0.020708349 = product of:
      0.041416697 = sum of:
        0.041416697 = weight(_text_:22 in 3211) [ClassicSimilarity], result of:
          0.041416697 = score(doc=3211,freq=2.0), product of:
            0.17841205 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.05094824 = 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
  9. Didegah, F.; Thelwall, M.: Co-saved, co-tweeted, and co-cited networks (2018) 0.01 
    0.010354174 = product of:
  0.020708349 = sum of:
    0.020708349 = product of:
      0.041416697 = sum of:
        0.041416697 = weight(_text_:22 in 4291) [ClassicSimilarity], result of:
          0.041416697 = score(doc=4291,freq=2.0), product of:
            0.17841205 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.05094824 = 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
  10. Thelwall, M.; Buckley, K.; Paltoglou, G.; Cai, D.; Kappas, A.: Sentiment strength detection in short informal text (2010) 0.01 
    0.008628479 = product of:
  0.017256958 = sum of:
    0.017256958 = product of:
      0.034513917 = sum of:
        0.034513917 = weight(_text_:22 in 4200) [ClassicSimilarity], result of:
          0.034513917 = score(doc=4200,freq=2.0), product of:
            0.17841205 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.05094824 = 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)
  0.5 = coord(1/2)
    Date
    22. 1.2011 14:29:23
  11. Thelwall, M.; Sud, P.; Wilkinson, D.: Link and co-inlink network diagrams with URL citations or title mentions (2012) 0.01 
    0.008628479 = product of:
  0.017256958 = sum of:
    0.017256958 = product of:
      0.034513917 = sum of:
        0.034513917 = weight(_text_:22 in 57) [ClassicSimilarity], result of:
          0.034513917 = score(doc=57,freq=2.0), product of:
            0.17841205 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.05094824 = 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)
  0.5 = coord(1/2)
    Date
    6. 4.2012 18:16:22
  12. Li, X.; Thelwall, M.; Kousha, K.: ¬The role of arXiv, RePEc, SSRN and PMC in formal scholarly communication (2015) 0.01 
    0.008628479 = product of:
  0.017256958 = sum of:
    0.017256958 = product of:
      0.034513917 = sum of:
        0.034513917 = weight(_text_:22 in 2593) [ClassicSimilarity], result of:
          0.034513917 = score(doc=2593,freq=2.0), product of:
            0.17841205 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.05094824 = 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)
  0.5 = coord(1/2)
    Date
    20. 1.2015 18:30:22
  13. Thelwall, M.: Are Mendeley reader counts high enough for research evaluations when articles are published? (2017) 0.01 
    0.008628479 = product of:
  0.017256958 = sum of:
    0.017256958 = product of:
      0.034513917 = sum of:
        0.034513917 = weight(_text_:22 in 3806) [ClassicSimilarity], result of:
          0.034513917 = score(doc=3806,freq=2.0), product of:
            0.17841205 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.05094824 = 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

Authors

Themes