Modeling the co-citation dependence on semantic layers of co-cited documents
ISSN: 1468-4527
Article publication date: 12 May 2021
Issue publication date: 25 January 2022
Abstract
Purpose
Co-citation frequency, defined as the number of documents co-citing two articles, is considered as a quantitative, and thus, an efficient proxy of subject relatedness or prestige of the co-cited articles. Despite its quantitative nature, it is found effective in retrieving and evaluating documents, signifying its linkage with the related documents' contents. To better understand the dynamism of the citation network, the present study aims to investigate various content features giving rise to the measure.
Design/methodology/approach
The present study examined the interaction of different co-citation features in explaining the co-citation frequency. The features include the co-cited works' similarities in their full-texts, Medical Subject Headings (MeSH) terms, co-citation proximity, opinions and co-citances. A test collection is built using the CITREC dataset. The data were analyzed using natural language processing (NLP) and opinion mining techniques. A linear model was developed to regress the objective and subjective content-based co-citation measures against the natural log of the co-citation frequency.
Findings
The dimensions of co-citation similarity, either subjective or objective, play significant roles in predicting co-citation frequency. The model can predict about half of the co-citation variance. The interaction of co-opinionatedness and non-co-opinionatedness is the strongest factor in the model.
Originality/value
It is the first study in revealing that both the objective and subjective similarities could significantly predict the co-citation frequency. The findings re-confirm the citation analysis assumption claiming the connection between the cognitive layers of cited documents and citation measures in general and the co-citation frequency in particular.
Peer review
The peer review history for this article is available at https://publons.com/publon/10.1108/OIR-04-2020-0126.
Keywords
Citation
Yaghtin, M., Sotudeh, H., Nikseresht, A. and Mirzabeigi, M. (2022), "Modeling the co-citation dependence on semantic layers of co-cited documents", Online Information Review, Vol. 46 No. 1, pp. 59-78. https://doi.org/10.1108/OIR-04-2020-0126
Publisher
:Emerald Publishing Limited
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