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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.

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.

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.

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.

The peer review history for this article is available at https://publons.com/publon/10.1108/OIR-04-2020-0126.

There are different opinions about the quality and reputation of publications. Given that one of the ways to analyze the quality of journals and articles is citation indicators, the purpose of this paper is to compare the citation indicators of open access (OA), full subscription and hybrid journals and articles based on the Scopus in the field of library and information science (LIS) for a period of ten years (2004-2013).

The research population is all LIS journals and articles in LIS hybrid journals in Scopus. The data related to citation indicators (number of received citations, two years’ impact, Citescore [IPP] and H-index) were extracted from Scopus, Journal Metrics and SCImago and analyzed using descriptive and analytical statistics with SPSS and Excel.

The findings indicate that number of OA journals and articles were less than that of full subscription and hybrid journals. The average rate of all citation indicators in hybrid journals were more than that of OA and full subscription journals. However, in the level of articles, the average number of citations received by OA articles (15.6) was more than that of non-OA articles (2.25).

Unlike journals, OA seems to be an advantage for articles in LIS. LIS hybrid journals receive more citations than OA and subscription journals. Thus, the authors’ approach to publishing in OA, hybrid or subscription journals and the publishers approach to the publication type may also be influenced by the findings of the present study.