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An automatic method to identify citations to journals in news stories: A case study of UK newspapers citing Web of Science journals
Kousha, Kayvan Thelwall, Mike
Kousha, Kayvan
Thelwall, Mike
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2019-08-30
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Purpose: Communicating scientific results to the public is essential to inspire future researchers and ensure that discoveries are exploited. News stories about research are a key communication pathway for this and have been manually monitored to assess the extent of press coverage of scholarship. Design / methodology /Approach: To make larger scale studies practical, this paper introduces an automatic method to extract citations from newspaper stories to large sets of academic journals. Curated ProQuest queries were used to search for citations to 9,639 Science and 3,412 Social Science Web of Science (WoS) journals from eight UK daily newspapers during 2006-2015. False matches were automatically filtered out by a new program, with 94% of the remaining stories meaningfully citing research. Findings: Most Science (95%) and Social Science (94%) journals were never cited by these newspapers. Half of the cited Science journals covered medical or health-related topics, whereas 43% of the Social Sciences journals were related to psychiatry or psychology. From the citing news stories, 60% described research extensively and 53% used multiple sources, but few commented on research quality. Research Limitations: The method has only been tested in English and from the ProQuest Newspapers database. Practical implications: Others can use the new method to systematically harvest press coverage of research. Originality /value: An automatic method was introduced and tested to extract citations from newspaper stories to large sets of academic journals.
Citation
Kousha, K. and Thelwall, M. (2019) An Automatic Method to Identify Citations to Journals in News Stories: A Case Study of UK Newspapers Citing Web of Science Journals, Journal of Data and Information Science, 4(3), pp. 73–95.
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Journal article
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en
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2096-157X
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Attribution-NonCommercial-NoDerivs 3.0 United States