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Despite the widespread studies on the attitudes about OA, there exists little comparative evidence about the opinions of author and non-author parties at a global level in a social context. To bridge the gap, this study first investigated the opinions of the users who posted at least one tweet about OA in 2019. Then, it zoomed in to explore the views of the OA-interested tweeters, i.e. the users who have posted five or more tweets about OA.

Using a content analysis method, with an opinion-mining approach, this study examined a sample of 9,268 OA-related tweets posted by 5,227 tweeters in 2019. The sentiments were analyzed using SentiStrength. A threshold of at least five tweets was set to identify the OA-interested tweeters.

Academics and scholars, library and information professionals, and journals and publishers were the main OA-interested tweeters, implying that OA debates have not been widely propagated from its traditional audience to the general public. Despite an overall positive attitude, the tweeters showed negative perspectives about the gold and hybrid models, validity and quality, and costs and funds. The negativity depended on the OA features tweeted, the tweeters' occupations and gender, as well as the trends.

The low societal impact of the OA debates calls for solutions to attract the public's attention and to exploit their potential to achieve the OA ideals. The OA stakeholders' divergence necessitates finding solutions to remedy the pitfalls. It also underlines the need for scrutiny into social layers when studying society's opinions and behaviors in a social network.

This is the first study in estimating the extent of the societal impact of OA debates, comparing the social OA stakeholders' opinions and their dependence on the OA features tweeted, the tweeter roles and gender and the tweet trending status.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-09-2022-0502

This study aimed to investigate how the enrichment of medical documents' index terms by their comments improves the relevance and novelty of the top-ranked results retrieved by an NLP system.

A semi-experimental pre-test and post-test research was designed to compare NLP-based indexes before and after being expanded by the comment terms. The experiments were conducted on a test collection of 13,957 documents commented by F1000-Prime reviewers. They were indexed at title, abstract, body and full-text levels. In total, 100 seed documents were randomly selected and served as queries. The textual similarity of the documents and queries was calculated using Lucene-more-like-this function and evaluated by the semantic similarity of their MeSH. The results novelty was measured using maximal marginal relevance and evaluated by their MeSH novelties. Normalized discounted cumulative gain was used to compare the basic and expanded indexes' precisions at 10, 20 and 50 top ranks.

The relevance and novelty of the results ranked at the top precision points was improved after expanding the indexes by the comment terms. The finding implies that meta-texts are effective in representing their mother documents, by adding dynamic elements to their rather static contents. It also provides further evidence about the merits of the application of social intelligence and collective wisdom reflected in the actions and reactions of users in tackling the challenges faced by NLP-based systems.

This is the first study to confirm that social comments on scientific papers improve the performance of information systems in terms of relevance and novelty.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-05-2022-0283.

The main objective of the present study is to determine the role of citation-based metrics (PageRank and HITS’ authority and hub scores) and non-citation metrics (Goodreads readers, reviews and ratings, textbook edition counts) in predicting educational ranks of textbooks.

The rankings of 1869 academic textbooks of various disciplines indexed in Scopus were extracted from the Open Syllabus Project (OSP) and compared with normalized counts of Scopus citations, scores of PageRank, authority and hub (HITS) in Scopus book-to-book citation network, Goodreads ratings and reviews, review sentiment scores and WorldCat book editions.

Prediction of the educational rank of scholarly syllabus books ranged from 32% in technology to 68% in philosophy, psychology and religion. WorldCat editions in social sciences, medicine and technology, Goodreads ratings in humanities, and book-citation-network authority scores in law and political science accounted for the strongest predictions of the educational score. Thus, each indicator of editions, Goodreads ratings, and book citation authority score alone can be used to show the rank of the academic textbooks, and if used in combination, they will help explain the educational uptake of books even better.

This is the first study examining the role of citation indicators, Goodreads readers, reviews and ratings in predicting the OSP rank of academic books.

Social comments are rich in information and useful in evaluating, ranking or retrieving different kinds of materials. However, their merits in representing or providing added values to scientific articles have not yet been studied. Therefore, the present study investigates the informativeness of open review reports as a kind of social comments in a scholarly setting.

A test collection was built consisting of 100 randomly selected queries, 1,962 reviewed documents and their reviewers' open reports from F1000Research. They were analyzed using natural language techniques. The comments' salient words were compared to the documents' and also to the Medical Subject Headings (MeSH) salient words. The receiver operating characteristic (ROC) curve was used to test the accuracy of the comments in representing their related articles.

The papers' contents and comments have a considerable number of salient words in common. The comments' salient words are also largely found in the MeSH, signifying their consistency with the knowledge tree and their potential to add some complementary features to their related items. The ROC curves confirm the accuracy of the comments in retrieving their related papers.

This research is the first to reveal the merits of open review reports on scientific papers, in terms of their relatedness to their mother articles, in specific, and to the knowledge tree, in general. They are found informative in not only representing the reviewed papers but also in adding values to the contents of the papers.

Considering the shortcomings of the ASIS&T Web-based thesaurus in representing concepts and semantic relations, there is a need to use more effective semantic tools, such as ontologies. The purpose of this paper is to build a prototype ontology (ASIS&TOnto) based on a Web-based thesaurus.

The prototype ontology was built based on the ASIS&T Web-based thesaurus by using the methodology developed by Noy and McGuinness (2001) and with the use of Protégé 4.3.3. With regard to the purpose of the study and the massive amount of concepts represented in the ASIS&T thesaurus, the focus of term selection for creating the core source of vocabulary for the prototype ontology was the “searching” area. The knowledge engineering approach (Na and Neoh, 2008) was used to extract concepts and semantic relations. The criterion for extracting semantic relations from each concept pair was one-sentence statement, one-paragraph statement and one-page statement. Finally, the extracted relations were analysed by subject experts.

Based on the findings of this research, the possibility of using the methodology developed by Noy and McGuinness (2001) for building an ontology based on a Web-based thesaurus in the field of LIS was investigated and the prototype ontology (ASIS&TOnto) was constructed in the area of “searching”.

The primary implication of ASIS&TOnto is aligned with Web 3.0 research where implications of semantic modelling are a priority for community equity of access to information as a basic human right. ASIS&TOnto ends up with an ontology that is comprehensive, at least with respect to expressivity of the current ASIS&T thesaurus user interface definition languages; that is, it can be universal and can be extendable to future user interfaces that do not exist at the moment.

For the first time, a new enriched semantic tool (ASIS&TOnto) was constructed based on the ASIS&T Web-based thesaurus.

The purpose of this paper is to investigate indexers’ evaluation on the usability of ontology vs thesaurus in representation of concepts and semantic relations. To do so, “searching” category of ASIS&T thesaurus was selected and ASIS&TOnto was built based on it.

The usability examination method is used in order to compare the two semantic tools. Nine indexers were recruited as participants, who were proficient in English language, had experience in using the thesaurus and all had successfully passed the course of “information representation.” They were asked to think aloud while working with the tools and to answer a semi-structured interview. The data gathering was continued until it reached its saturation point.

The results of this study revealed that the definitions and scope notes represented in indexing tools such as thesauri and ontologies have an important role in improvement of indexers’ understanding. On comparing the hierarchical relations, results show that converting the structure of hierarchical relationships of ASIS&T thesaurus can enhance the indexers understanding of them, and also enriching the associative relations of ASIS&T thesaurus can cause indexers to have a better understanding and evaluation of the presented concepts and relations.

This study shares our findings on the usability of ASIS&T thesaurus as a core set of vocabulary for building a “searching” domain as a prototype ontology in the area of library and information science and provides the indexers viewpoints of the two semantic tools in this area.

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.

The purpose of this study is to attempt to suggest an adjustment in Iran's national publication strategy based on the country‐specific Matthew core journals. It investigates Iran's performance in its national journal set, and proposes a more prominent journal subset.

A citation analysis method is applied to study Iran's scientific performance in its national journal set. The data were extracted from the Science Citation Index at Web of Science and JCR and imported to SPSS for further refinement and analysis.

The results showed that Iran experienced comparatively considerable citation loss. Surplus citations are concentrated in a small number of journals, presented as Iran's positive Matthew core journals. The results also confirm a relatively poor publication strategy adopted by Iranian scientists and that a publication concentration does not necessarily enhance the chance of being widely cited.

These findings imply that Iran needs to watch more vigilantly the functioning of its science system. To improve its presence at the international level, Iran should re‐orient its publication strategy towards a more prominent one. This may be the case for similar science systems, where the emphasis is given to quantity rather than quality.

Country‐specific Matthew core journals, with serious citation competition, can serve as an important criterion to monitor the functioning of science systems regarding publication strategy. This is the first empirical study to employ the concept to suggest improvements in a country's publication strategy.

The purpose of this paper is to describe a bibliometric study of journal articles in Social Sciences Citation Index (SSCI) related to open access journals. Publication trends from 1993 to 2016, core journals, prolific authors and article citation frequencies were examined to provide librarians and scholars with a basic reference of relevant resources.

Among articles published in journals which have been listed in SSCI during 1993-2016, titles were searched for the term “open access journal”. The data were evaluated in response to five research questions concerning topics including publication trends from 1993 to 2016, core journals, prolific authors and article citation frequencies.

In total, 115 articles on open access journals were authored by 215 individuals from 1993 to 2016. The 115 articles analyzed were cited 842 times in 57 journals.

This paper describes a bibliometric study on open access journal publication, which has rarely been performed within the specific context of document supply issues. It provides a foundation for further research on titles as well as resources for librarians and scholars.