Search (4 results, page 1 of 1)

0.0071777035 = product of:
  0.10048784 = sum of:
    0.10048784 = weight(_text_:media in 3781) [ClassicSimilarity], result of:
      0.10048784 = score(doc=3781,freq=12.0), product of:
        0.13212246 = queryWeight, product of:
          4.6838713 = idf(docFreq=1110, maxDocs=44218)
          0.02820796 = queryNorm
        0.76056594 = fieldWeight in 3781, product of:
          3.4641016 = tf(freq=12.0), with freq of:
            12.0 = termFreq=12.0
          4.6838713 = idf(docFreq=1110, maxDocs=44218)
          0.046875 = fieldNorm(doc=3781)
  0.071428575 = coord(1/14)

Abstract

Social media has become integrated into the fabric of the scholarly communication system in fundamental ways, principally through scholarly use of social media platforms and the promotion of new indicators on the basis of interactions with these platforms. Research and scholarship in this area has accelerated since the coining and subsequent advocacy for altmetrics-that is, research indicators based on social media activity. This review provides an extensive account of the state-of-the art in both scholarly use of social media and altmetrics. The review consists of 2 main parts: the first examines the use of social media in academia, reviewing the various functions these platforms have in the scholarly communication process and the factors that affect this use. The second part reviews empirical studies of altmetrics, discussing the various interpretations of altmetrics, data collection and methodological limitations, and differences according to platform. The review ends with a critical discussion of the implications of this transformation in the scholarly communication system.

0.0042295023 = product of:
  0.059213027 = sum of:
    0.059213027 = weight(_text_:media in 1229) [ClassicSimilarity], result of:
      0.059213027 = score(doc=1229,freq=6.0), product of:
        0.13212246 = queryWeight, product of:
          4.6838713 = idf(docFreq=1110, maxDocs=44218)
          0.02820796 = queryNorm
        0.44816777 = fieldWeight in 1229, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          4.6838713 = idf(docFreq=1110, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1229)
  0.071428575 = coord(1/14)

Abstract

Data collected by social media platforms have been introduced as new sources for indicators to help measure the impact of scholarly research in ways that are complementary to traditional citation analysis. Data generated from social media activities can be used to reflect broad types of impact. This article aims to provide systematic evidence about how often Twitter is used to disseminate information about journal articles in the biomedical sciences. The analysis is based on 1.4 million documents covered by both PubMed and Web of Science and published between 2010 and 2012. The number of tweets containing links to these documents was analyzed and compared to citations to evaluate the degree to which certain journals, disciplines, and specialties were represented on Twitter and how far tweets correlate with citation impact. With less than 10% of PubMed articles mentioned on Twitter, its uptake is low in general but differs between journals and specialties. Correlations between tweets and citations are low, implying that impact metrics based on tweets are different from those based on citations. A framework using the coverage of articles and the correlation between Twitter mentions and citations is proposed to facilitate the evaluation of novel social-media-based metrics.

0.002930285 = product of:
  0.04102399 = sum of:
    0.04102399 = weight(_text_:media in 2502) [ClassicSimilarity], result of:
      0.04102399 = score(doc=2502,freq=2.0), product of:
        0.13212246 = queryWeight, product of:
          4.6838713 = idf(docFreq=1110, maxDocs=44218)
          0.02820796 = queryNorm
        0.31049973 = fieldWeight in 2502, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.6838713 = idf(docFreq=1110, maxDocs=44218)
          0.046875 = fieldNorm(doc=2502)
  0.071428575 = coord(1/14)

Abstract

This brief communication presents preliminary findings on automated Twitter accounts distributing links to scientific articles deposited on the preprint repository arXiv. It discusses the implication of the presence of such bots from the perspective of social media metrics (altmetrics), where mentions of scholarly documents on Twitter have been suggested as a means of measuring impact that is both broader and timelier than citations. Our results show that automated Twitter accounts create a considerable amount of tweets to scientific articles and that they behave differently than common social bots, which has critical implications for the use of raw tweet counts in research evaluation and assessment. We discuss some definitions of Twitter cyborgs and bots in scholarly communication and propose distinguishing between different levels of engagement-that is, differentiating between tweeting only bibliographic information to discussing or commenting on the content of a scientific work.

0.0024419043 = product of:
  0.034186658 = sum of:
    0.034186658 = weight(_text_:media in 678) [ClassicSimilarity], result of:
      0.034186658 = score(doc=678,freq=2.0), product of:
        0.13212246 = queryWeight, product of:
          4.6838713 = idf(docFreq=1110, maxDocs=44218)
          0.02820796 = queryNorm
        0.25874978 = fieldWeight in 678, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.6838713 = idf(docFreq=1110, maxDocs=44218)
          0.0390625 = fieldNorm(doc=678)
  0.071428575 = coord(1/14)

Abstract

Online videos provide a novel, and often interactive, platform for the popularization of science. One successful collection is hosted on the TED (Technology, Entertainment, Design) website. This study uses a range of bibliometric (citation) and webometric (usage and bookmarking) indicators to examine TED videos in order to provide insights into the type and scope of their impact. The results suggest that TED Talks impact primarily the public sphere, with about three-quarters of a billion total views, rather than the academic realm. Differences were found among broad disciplinary areas, with art and design videos having generally lower levels of impact but science and technology videos generating otherwise average impact for TED. Many of the metrics were only loosely related, but there was a general consensus about the most popular videos as measured through views or comments on YouTube and the TED site. Moreover, most videos were found in at least one online syllabus and videos in online syllabi tended to be more viewed, discussed, and blogged. Less-liked videos generated more discussion, although this may be because they are more controversial. Science and technology videos presented by academics were more liked than those by nonacademics, showing that academics are not disadvantaged in this new media environment.