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Library and Information Science Papers Discussed on Twitter: A new Network-based Approach for Measuring Public Attention

机译:Twitter讨论的图书馆和信息科学论文:一种新的基于网络的衡量公众注意力方法

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Purpose In recent years, one can witness a trend in research evaluation to measure the impact on society or attention to research by society (beyond science). We address the following question: can Twitter be meaningfully used for the mapping of public and scientific discourses? Design/methodology/approach Recently, Haunschild et al. (2019) introduced a new network-oriented approach for using Twitter data in research evaluation. Such a procedure can be used to measure the public discussion around a specific field or topic. In this study, we used all papers published in the Web of Science (WoS, Clarivate Analytics) subject category Information Science & Library Science to explore the publicly discussed topics from the area of library and information science (LIS) in comparison to the topics used by scholars in their publications in this area. Findings The results show that LIS papers are represented rather well on Twitter. Similar topics appear in the networks of author keywords of all LIS papers, not tweeted LIS papers, and tweeted LIS papers. The networks of the author keywords of all LIS papers and not tweeted LIS papers are most similar to each other. Research limitations Only papers published since 2011 with DOI were analyzed. Practical implications Although Twitter data do not seem to be useful for quantitative research evaluation, it seems that Twitter data can be used in a more qualitative way for mapping of public and scientific discourses. Originality/value This study explores a rather new methodology for comparing public and scientific discourses.
机译:目的近年来,人们可以见证研究评估的趋势,以衡量社会或注意社会研究的影响(超越科学)。我们解决以下问题:Twitter可以有意义地用于公共和科学致辞的映射吗?最近设计/方法/方法,Haunschild等。 (2019)推出了一种新的用于研究评估中的推特数据的新的面向网络的方法。这样的程序可用于测量特定字段或主题周围的公众讨论。在这项研究中,我们使用了在科学网络(WOS,Clarivate Analytics)主题信息科学与图书馆学科的所有论文,以探讨图书馆和信息科学领域(LIS)的公开讨论的主题与所使用的主题相比通过学者在这一领域的出版物。结果表明,LIS论文在Twitter上表示相当良好。类似的主题出现在所有LIS论文的作者关键词网络中,而不是发布的LIS论文,以及推文的LIS论文。所有LIS论文的作者关键词的网络和不推特的LIS文件彼此最相似。研究限制只分析了自2011年以来发表的论文。实际意义虽然Twitter数据似乎对定量研究评估似乎并不有用,但似乎可以以更具定性的方式使用,以便更具定性的方式来绘制公共和科学的歧视。原创性/价值本研究探讨了比较公共和科学歧视的相当新的方法。

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