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Evaluating academic papers and groups is important in scholar evaluation and literature retrieval. However, current evaluation indices, which pay excessive attention to the citation number rather than the citation importance and unidirectionality, are relatively simple. This study proposes new evaluation indices for papers and groups. First, an improved PageRank (PR) algorithm introducing citation importance is proposed to obtain a new citation-based paper index (CPI) via a pre-ranking and fine-tuning strategy. Second, to evaluate the paper's influence inside and outside its research field, the focus citation-based paper index (FCPI) and diversity citation-based paper index (DCPI) are proposed based on topic similarity and diversity, respectively. Third, aside from the statistical indices for academic papers, we propose a foreign academic degree of dependence (FAD) to characterise the dependence between two academic groups. Finally, artificial intelligence (AI) papers from 2005 to 2019 are utilised for a case study. [ABSTRACT FROM AUTHOR]

Quantifiable predictability in the citation counts of articles is significant in scientometrics and informetrics. Many metrics based on the citation counts can evaluate the scientific impact of research articles and journals. Utilising time series models, an article's citation counts up to the y th year after publication can be predicted by those up to the previous years. However, the typically used models cannot predict the fat tail of the actual citation distributions. Thus, based on cumulative advantage of the citation behaviour, we propose a method to predict the accumulated citation counts, by using a random number sampled from a power-law distribution to modify the results given by a recurrent neural network (RNN), long short-term memory. Extensive experiments on the data set including 17 journals in information science verified the effectiveness of our method by the good fittings on distributions and evolutionary trends of the citation counts of articles. Our method has the potential to be extended to predict other popular assessment measures such as impact factor and h -index for journals. [ABSTRACT FROM AUTHOR]

In the era of print reading, being selected as a cover paper holds a crucial role in attracting greater attention and bolstering academic influence. It is important to assess its effect on scholarly attention and academic influence, particularly in light of the evolving reading habits among researchers. In this study, we empirically estimate the impact of ‘being selected as a cover paper’ on scholarly online attention (proxied by altmetric score) and academic influence (measured by citation counts). This analysis is based on a data set comprising 25,238 papers selected from 10 high-impact materials science journals (with journal impact factors exceeding 10) published between 2016 and 2020. Our findings indicate a positive correlation between ‘being selected as a cover paper’ and scholarly online attention, while its impact on academic influence is insignificant. Our results remain robust even when excluding the top 1% mostly cited papers, employing the negative binomial model and considering various time windows for estimation. Heterogeneity analysis indicates that the impact of ‘being selected as a cover paper’ on scholarly online attention holds across nearly all topics, consistent with the baseline result. In addition, online platforms, such as Twitter and News outlets, exhibit a higher frequency of sharing research featured as cover papers. We offer suggestive evidence that ‘being selected as a cover paper’ is not solely contingent on its quality. These findings contribute to the development of a precise, dynamic and multi-dimensional evaluation framework, crucial for navigating the revolution of science communication. [ABSTRACT FROM AUTHOR]

Patent citations received by a paper are considered one of the most appropriate indicators for quantifying the technological impact of scientific research. In light of the large number of published research outcomes, technology developers need an effective method to identify academic work with potential technological impact and so as to provide scientific theories for the generation of relevant technologies. Focusing on the technical field of artificial intelligence (AI), this study constructs a set of 47 features from seven dimensions and uses feature selection and machine learning models to accurately predict how research papers impact AI technology. The results show that the random forest model is superior to the other tested models in predicting AI patent citations of papers, with citation-related features (such as ‘PaperCitations’ and ‘Background’) playing a vital role in the prediction. [ABSTRACT FROM AUTHOR]

This study examines the interdisciplinarity scores of papers published in five major Economic journals by analysing their references. It also explores the relationship between interdisciplinarity and citation. The study considers the influence of the citation time window on accumulating citations and investigates the source of citation advantage for high interdisciplinarity papers. Empirical findings reveal a U-shaped curve relationship between the interdisciplinarity of papers and their citation frequency. Papers with high interdisciplinarity do enjoy a citation advantage, which primarily stems from the attention and citations from distant disciplinary papers and multidisciplinary journals. However, it often takes a longer time for the value of interdisciplinary papers to be recognised. Based on these findings, the study discusses the necessity and effectiveness of incentives for interdisciplinary research and provides recommendations for evaluating and managing interdisciplinary research. [ABSTRACT FROM AUTHOR]

The large amount of scientific papers provides a convenient way for users to know the latest research progress of a specific research topic. However, the large volume and the diverse research themes hiding among these papers usually hinder users from conveniently locating the specific papers that they are interested in. To tackle this problem, we propose a novel unsupervised cascade learning scheme that aims to extract a ‘cluster-theme keywords’ structure from the related papers of a research topic so as to help users locate their research interests quickly. Our approach first selects some representative papers for a research topic. It then clusters these selected papers into several small clusters with the help of a domain ontology. It finally extracts some theme keywords for each cluster. Our approach not only greatly reduces the time-consuming and labour-intensive paper-seeking process for users, but also comprehensively displays the diverse themes of a research topic. We conducted extensive experiments to evaluate our proposed approach. The experimental results demonstrate the effectiveness of this approach, which produces promising results. [ABSTRACT FROM PUBLISHER]

Discusses global paper consumption and the influence of electronic information media from the perspective of information science and the paper industry. Analysis of market segments for printing and writing papers; Uneven distribution of paper consumption around the world; Impact of information technology on paper consumption in developing countries.

A series of experiments designed to examine the most effective way of presenting information in a list of search results, found that presenting the subject of the paper can be of benefit to the user. Several sources can be used to find the paper's subject, including citation elements found in a list of papers. The purpose of the study was to examine what constitutes the optimal elements in a list of citations for finding the paper's subject. The value of terms derived from the papers cited in the source article (title) and key works are ranked highest while journal name and author are less informative. [ABSTRACT FROM AUTHOR]

Research topics vary in their citation potential. In a metric-wise scientific milieu, it would be probable that authors tend to select citation-attractive topics especially when choosing open access (OA) outlets that are more likely to attract citations. Applying a matched-pairs study design, this research aims to examine the role of research topics in the citation advantage of OA papers. Using a comparative citation analysis method, it investigates a sample of papers published in 47 Elsevier article processing charges (APC)-funded journals in different access models including non-open access (NOA), APC, Green and mixed Green-APC. The contents of the papers are analysed using natural language processing techniques at the title and abstract level and served as a basis to match the NOA papers to their peers in the OA models. The publication years and journals are controlled for in order to avoid their impacts on the citation numbers. According to the results, the OA citation advantage that is observed in the whole sample still holds even for the highly similar OA and NOA papers. This implies that the OA citation surplus is not an artefact of the OA and NOA papers' differences in their topics and, therefore, in their citation potential. This leads to the conclusion that OA authors' self-selectivity, if it exists at all, is not responsible for the OA citation advantage, at least as far as selection of topics with probably higher citation potentials is concerned. [ABSTRACT FROM AUTHOR]

Tech mining is the application of text mining tools to science and technology information resources. The ever-increasing volume of scientific outputs is a boom to technological innovation, but it also complicates efforts to obtain useful and concise information for problem solving. This challenge extends to tech mining, where the development of techniques compatible with big data is an urgent issue. This article introduces a semi-supervised method for extracting layered technological information from scientific papers in order to extend the reach of tech mining. Our method starts with several pre-set seed patterns used to extract candidate phrases by matching the dependency tree of each sentence. Then, after a series of judgements, phrases are divided into two categories: 'main technique' and 'tech-component'. (A technique, for the purposes of this study, is a method or tool used in the article being analysed.) In order to generate new patterns for subsequent iterations, a weighted pattern learning method is also adopted. Finally, multiple iterations of the method are applied to extract technological information from each paper. A dataset from the field of optical switcher is used to verify the method's effectiveness. Our findings are that (1) by two loops of extraction process in each iteration, our method realises the layered technological information extraction, which contains the 'part–whole' relationships between main techniques and tech-components; (2) the recall rate for main techniques is superior to the baseline after iterating 23 rounds; (3) when layering is disregarded, in the aspect of the precision and the volume of techniques, the new method is higher than that for the baseline; and (4) adjusting another two parameters can optimise the efficiency – however, the effect is neither pronounced nor straightforward. [ABSTRACT FROM AUTHOR]

This article defines and explores the direct citations between citing publications (DCCPs) of a publication. We construct an ego-centred citation network for each paper that contains all of its citing papers and itself, as well as the citation relationships among them. By utilising a large-scale scholarly dataset from the computer science field in the Microsoft Academic Graph (MAG-CS) dataset, we find that DCCPs exist universally in medium and highly cited papers. For those papers that have DCCPs, DCCPs do occur frequently; highly cited papers tend to contain more DCCPs than others. Meanwhile, the number of DCCPs of papers published in different years does not vary dramatically. This paper also discusses the relationship between DCCPs and some indirect citation relationships (e.g. co-citation and bibliographic coupling). [ABSTRACT FROM AUTHOR]

In this study, we investigated whether female and male authors in the field of life sciences and biomedicine differed in their tendency for citation and citation sentiment. The data comprised two sets, cited set and citing set. Cited set comprised 17,237 articles whereas citing set comprised 115,935 articles. The cited set which is from the area Life Sciences & Biomedicine and published during 2012–2016 was retrieved from the Web of Science Medline. The citing set and its citation contexts were retrieved using the Colil database. The analysis was done using a combination of homophily analysis, regression analysis and a chi-square test. The covariates in the regression analyses were features related to authors, journal, institution, country and abstract readability. The homophily analysis showed a significant tendency for female (8%) and male (14%) authorship teams to cite papers by the same gender composition teams. In addition, the results of regression analysis (Model 1) and pairwise comparisons showed that male-authored papers received a significant higher positive sentiment compared with female-authored papers. The results of regression analysis (Model 2) showed a small significant positive association between gender similarity of cited and citing authorship teams and the sentiment score. However, further analysis using the chi-square test showed a significant lower tendency for women to use positive terms when citing the research findings of papers with the same gender composition. Men, in contrast, used significantly more positive terms when citing papers with the same gender composition. Finally, lay summary for a cited paper, country similarity and the venue of cited publication when it was a mega journal had a positive significant association with the sentiment score received. [ABSTRACT FROM AUTHOR]

Scholars routinely search relevant papers to discover and put a new idea into proper context. Despite ongoing advances in scholarly retrieval technologies, locating relevant papers through keyword queries is still quite challenging due to the massive expansion in the size of the research paper repository. To tackle this problem, we propose a novel real-time feedback query expansion technique, which is a two-stage interactive scholarly search process. Upon receiving the initial search query, the retrieval system provides a ranked list of results. In the second stage, a user selects a few relevant papers, from which useful terms are extracted for query expansion. The newly expanded query is run against the index in real time to generate the final list of research papers. In both stages, citation analysis is involved in further improving the quality of the results. The novelty of the approach lies in the combined exploitation of query expansion and citation analysis that may bring the most relevant papers to the top of the search results list. The experimental results on the Association of Computational Linguistics (ACL) Anthology Network data set demonstrate that this technique is effective and robust for locating relevant papers regarding normalised discounted cumulative gain (nDCG), precision and recall rates than several state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

Aims. The aims of this study were (i) to determine the success rate for obtaining conference papers by using e-mail, and (ii) to compare the results with those found previously for reprints of published papers. Method. 100 requests for copies of papers delivered at a conference in the USA were made by e-mail from the UK. A reminder was sent to the appropriate authors if no paper had been received within three weeks. Results. 54 papers were received within six weeks. Most came by return or within one day, and all came electronically. Few of these papers could be described as conference papers, however, as the majority of them were copies of papers prepared for publication elsewhere. In addition, many lacked the information necessary for them to be adequately cited in other publications. [ABSTRACT FROM AUTHOR]

As from 1984, synopses based on unpublished research reports have been published in the Netherlands Journal of Agricultural Science (NJAS), in addition to full papers unre lated to the synopses.To study readers' behaviour towards the synopsis, which is still a relatively uncommon vehicle of primary communication, both full papers (n = 94) and synopses (n = 67) published in NJAS in 1984-1986 were subjected to citation analysis. Self-ci tations ("autocitations," as opposed to "allocitations") were excluded from most analyses. On average, citation yields were significantly lower for synopses than for full papers. The citation yields vaned strongly between the three years.The distribution of the journal's contents over the various subdisciplines of agricultural science explamed fairly well the capricious behaviour of citation yields over the years, but not the difference in yield between synopses and full papers.Further, the coverage of NJAS papers by five major biblio graphic databases was analysed. AGRIS and SCISEARCH covered NJAS' contents integrally. Thirteen percent of all papers (n = 21) covered by AGRIS and SCISEARCH only had not been cited at all. CHEMABS' coverage appeared to be fmrly consistent with the scope of this database (chemistry). However, BIOSIS' and CABI's coverage behaviour over the period in question appeared to be characterized by incon sistency and arbitrariness. Coverage by BIOSIS and CABI was significantly better for full papers than for synopses (BIOSIS 87% and 43%. and CABI 65% and 48%, respectively).After correction for bibliographic coverage the citation yield was still lower for synopses than for full papers, but the difference was no longer significant. Two-thirds of the initial difference in citation yield between full papers and synopses could be attributed to differences in bibliographic coverage.It is concluded that inconsistent bibliographic coverage procedures seriously undermine the penetrative capacity (and hence meaningfulness) of papers published in journals whose readers depend largely on bibliographic sources. [ABSTRACT FROM PUBLISHER]

Identifies personal transferable skills that employers feel are necessary in the information professions and lacking in graduates of information science programs, including communication skills. The discussion covers the effects of this lack, the need for departments of library and information science to address the problem, and possible methods for teaching such skills. (12 references) (CLB)

Paper has been the format of choice for disseminating geographic information for millennia; however the arrival of the internet and mobile technologies has created new modes of map consumption. This paper investigates the future role of paper mapping in a society where access to online digital mapping is freely available. The research consists of an online survey to investigate how people use and view online/digital and paper maps, together with a task-based user study to investigate the suitability of each map format for a variety of given tasks. Similar to previous research, we categorize participants into two groups (experts and non-experts/novices) based on their level of geographic skill. Results demonstrate that geographic knowledge and the context of use clearly relate to preference of map format. As geographic skill increases, there is a greater preference for paper maps; non-experts prefer online/digital maps. Also, paper is the preferred medium for planning and executing navigation on foot across all groups. Overall, results suggest that paper maps are here to stay for the foreseeable future and remain the preferred choice for some in the digital age. [ABSTRACT FROM PUBLISHER]

This paper analyses the reliability of the double-blind peer review systems used for submissions to the 2001 and 2002 UK Academy for Information Systems (UKAIS) conferences. The level of reliability found in the first conference was marginally lower than would be expected from a model based on chance. In the second conference the reliability level was significantly better, but still low. The paper explores some of the implications of this for the reviewing system, and suggests a model for assessing the impact of low levels of reliability. [ABSTRACT FROM AUTHOR]

Co-authorship networks consist of nodes and numerous links indicating scientific collaboration of researchers. These networks could be studied through social networks analysis and data mining techniques. The focus of the article is twofold: the first objective is the analysis of the co-authorship networks of the top 60 countries that had the highest number of scientific publications in the world, and the second one is the discovery of collaboration patterns of highly cited papers of these countries. To do so, all scientific publications of the top 60 countries in all fields as well as their highly cited papers were included in the study period between 2011 and 2015. The research samples in the first part included 10,460,999 documents and in the second part encompassed 711,025 highly cited papers. Required data were extracted from web of science database. To analyse co-authorship networks, centrality indices and clustering coefficient were used. UCINET, Pajek, VOSviewer and BibExcel software were used to map co-authorship networks of the countries and to calculate indices. Finally, the discovery of collaboration patterns in highly cited papers is studied through association rules. The research data indicated that over 95% of documents has been produced by the top 60 countries. In addition, the USA, Germany, England, France and Spain launched the most co-authorship. Quantitatively, there have been the most powerful collaboration links between China and the USA, the USA and England, the USA and Germany, and the USA and Canada. The clustering data indicated that collaborations of the top countries of the world were in three main clusters. The Friedman test showed that there was a significant difference in the priorities of the countries for collaboration; and the USA, China, England, Germany, France, Japan and Italy are in the top priority for collaboration, respectively. The results of collaboration pattern in highly cited papers indicated that the USA participates in more than half of collaboration patterns for producing highly cited papers. [ABSTRACT FROM AUTHOR]

In the era of the Internet and big data, the PageRank (PR) algorithm is a constantly evolving research field. However, there is no systematic research to explore the overall development trend of the PR domain. This article evaluates 1446 articles related to the PR algorithm and provides a thorough understanding of the PR field through the main path analysis (MPA). Through two basic main paths, a number of papers that play a leading role have been identified, which outline the backbone of the PR domain. Based on the analysis of multiple main paths, four main subareas have been investigated. There are accelerating the computation of PR, comprehensive applications of PR, researches on academic impact assessment and age preference in network evolution. Finally, this article discusses the research findings and the future directions of the PR field. It is the first attempt to identify the development trend of the PR domain through MPA, thus providing an insight into the knowledge evolution of the PR field over the past two decades. [ABSTRACT FROM AUTHOR]

The Semantic Web in general and the Linked Open Data Initiative, in particular, are a growing movement for organisations to make their existing data available in a machine-readable format. Thus, institutions are highly encouraged to publish, share and interlink their data publicly. The more data are opened on the Web (Open Data), the more integrated sets of data will be connected in the Semantic Web (Linked Open Data). Within this context, libraries can complement their data by linking it to other, external data sources. The purpose of this article is to identify papers that refer to linked data in libraries, emphasising the ways that linked data empower libraries to put their knowledge in the context of the open-world, thus enhancing semantic technology innovations. The study considered papers published between 2008 and 2019 in English and presents the collected literature by grouping it according to the topic each paper refers to. The results show that libraries are facing a period of continuing change which present several challenges and indicate that they are moving towards developing new practices, policies and services. [ABSTRACT FROM AUTHOR]

Researchers give credit to peer-reviewed, and thus, credible publications through citations. Despite a rigorous reviewing process, certain articles undergo retraction due to disclosure of their ethical or scientific deficiencies. It is, therefore, important to understand how society and academia react to the erroneous or deceitful claims and purge the science of their unreliable results. Applying a matched-pairs research design, this study examined a sample of medicine-related retracted and non-retracted articles matched by their content similarity. The regression analysis revealed similarities in obsolescence trends of the retracted and non-retracted groups. The Generalized Estimating Equations showed that citations are affected by the retraction status, life after retraction, life cycle and the journals' previous reputation, with the two formers being the strongest in positively predicting the citations. The retracted papers obtain fewer citations either before or after retraction, implying academia's watchful reaction to the low-quality papers even before official announcement of their fallibility. They exhibit an equal or higher social recognition level regarding Tweets and Blog Mentions, while a lower status regarding Mendeley Readership. This could signify social users' sensibility regarding scientific quality since they probably publicise the retraction and warn against the retracted items in their tweets or blogs, while avoiding recording them in their Mendeley profiles. Further scrutiny is required to gain insight into the sensibility, if any, about scientific quality. The study's originality relies on matching the retracted and non-retracted papers with their topics and neutralising variations in their citation potentials. It is also the first study comparing the groups' social impacts. [ABSTRACT FROM AUTHOR]

The co-opinionatedness measure, that is, the similarity of cociting documents in their opinions about their cocited articles, has been recently proposed. The present study uses a wider range of baselines and benchmarks to investigate the measure's effectiveness in retrieval ranking that was previously confirmed in a pilot study. A test collection was built including 30 seed documents and their 4702 cocited articles. Their citances and full-texts were analysed using natural language processing (NLP) and opinion mining techniques. Cocitation values, syntactical similarity and contexts similarity were used as baselines. The distributional semantic similarity and the linear and hierarchical Medical Subject Headings (MeSH) similarities served as benchmarks to evaluate the effect of the co-opinionatedness as a boosting factor on the performance of the baselines. The improvements in the rankings were measured by normalised discounted cumulative gain (nDCG). According to the findings, there existed significant differences between the nDCG mean values obtained before and after weighting the baselines by the co-opinionatedness measures. The results of the generalisability study corroborated the reliability and generalisability of the systems. Accordingly, the similarity in the opinions of the cociting papers towards their cocited articles can explain the cocitation relation in the scientific papers network and can be effectively utilised for improving the results of the cocitation-based retrieval systems. [ABSTRACT FROM AUTHOR]

Scientific research is typically communicated via papers in journals. To an outsider, the contents of these papers appear to by mystic and wonderful: to an insider, they convey rapidly and efficiently information about the research that has been done. Even to scientists, it may not be obvious that their papers provide the simplest way of communicating research. However, a detailed study of why papers are constructed as they are suggests that the lay-out is a consequence of a long evolution aimed at simplifying the complexity of scientific communica tion. [ABSTRACT FROM PUBLISHER]

There has been a proliferation of new research discovery tools that aid scientists in finding relevant publications. To obtain a general overview of this development, this article generates a conceptual typology of all possible research discovery tools by drawing from the information-theoretical concepts of redundancy/variety. Bibliometric links between scholarly publications can thus exhibit 'redundancy' (i.e. expectable linkages between academic works) or 'variety' (i.e. original co-occurrence patterns). On the redundancy-reproducing end of the typology are machines that harness extant co-citations or keyword queries, such as academic search engines and paper recommender systems. The variety end of the spectrum harbours services that enable categorial browsing or that suggest publications randomly, such as journals' tables of contents or random paper bots. The typology has implications for understanding how the design of research discovery platforms may ultimately shape aggregate citational networks of science. [ABSTRACT FROM AUTHOR]