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  • × author_ss:"Hellsten, I."
  • × author_ss:"Leydesdorff, L."
  1. Hellsten, I.; Leydesdorff, L.: ¬The construction of interdisciplinarity : the development of the knowledge base and programmatic focus of the journal Climatic Change, 1977-2013 (2016) 0.02 
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    Abstract
    Climate change as a complex physical and social issue has gained increasing attention in the natural as well as the social sciences. Climate change research has become more interdisciplinary and even transdisciplinary as a typical Mode-2 science that is also dependent on an application context for its further development. We propose to approach interdisciplinarity as a co-construction of the knowledge base in the reference patterns and the programmatic focus in the editorials in the core journal of the climate-change sciences-Climatic Change-during the period 1977-2013. First, we analyze the knowledge base of the journal and map journal-journal relations on the basis of the references in the articles. Second, we follow the development of the programmatic focus by analyzing the semantics in the editorials. We argue that interdisciplinarity is a result of the co-construction between different agendas: The selection of publications into the knowledge base of the journal, and the adjustment of the programmatic focus to the political context in the editorials. Our results show a widening of the knowledge base from referencing the multidisciplinary journals Nature and Science to citing journals from specialist fields. The programmatic focus follows policy-oriented issues and incorporates public metaphors.
    Date
    24. 8.2016 17:53:22
    Type
    a
  2. Hellsten, I.; Leydesdorff, L.: Automated analysis of actor-topic networks on twitter : new approaches to the analysis of socio-semantic networks (2020) 0.00 
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    Abstract
    Social media data provide increasing opportunities for the automated analysis of large sets of textual documents. So far, automated tools have been developed either to account for the social networks among participants in the debates, or to analyze the content of these debates. Less attention has been paid to mapping co-occurrences of actors (participants) and topics (content) in online debates that can be considered as socio-semantic networks. We propose a new, automated approach that uses the whole matrix of co-addressed topics and actors for understanding and visualizing online debates. We show the advantages of the new approach with the analysis of two data sets: first, a large set of English-language Twitter messages at the Rio?+?20 meeting, in June 2012 (72,077 tweets), and second, a smaller data set of Dutch-language Twitter messages on bird flu related to poultry farming in 2015-2017 (2,139 tweets). We discuss the theoretical, methodological, and substantive implications of our approach, also for the analysis of other social media data.
    Type
    a

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