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This article explores the factors influencing the future citations of conference papers. We concentrated on the explanatory power of early attention on conference papers for citations collected from Google Scholar and Scopus. The early attention data includes users' online activities in a conference support system: CN3. Bookmarks from the bibliographic management system, Citeulike, were used as a collateral source of early attention. To examine the chronological contributions of 13 factors on citations, a multiple sequential regression analysis was conducted for three timepoints of the publication cycle—paper submission, time of conferences, and months after conferences. Our results illustrate that online readers' early attention of Citeulike bookmarks were found to have the most influence on the future impact of the conference papers. The early attention records from CN3 made noteworthy improvements to explaining both the Google and Scopus citations as well. We also found that the type of papers the number of papers presented at a conference, and the best article award records were significant factors influencing future citations. However, the magnitude of the effects made by online readers' early attention from both sources appears to be larger than these three traditional factors. [ABSTRACT FROM AUTHOR]

Main path analysis is a popular method for extracting the scientific backbone from the citation network of a research domain. Existing approaches ignored the semantic relationships between the citing and cited publications, resulting in several adverse issues, in terms of coherence of main paths and coverage of significant studies. This paper advocated the semantic main path network analysis approach to alleviate these issues based on citation function analysis. A wide variety of SciBERT‐based deep learning models were designed for identifying citation functions. Semantic citation networks were built by either including important citations, for example, extension, motivation, usage and similarity, or excluding incidental citations like background and future work. Semantic main path network was built by merging the top‐K main paths extracted from various time slices of semantic citation network. In addition, a three‐way framework was proposed for the quantitative evaluation of main path analysis results. Both qualitative and quantitative analysis on three research areas of computational linguistics demonstrated that, compared to semantics‐agnostic counterparts, different types of semantic main path networks provide complementary views of scientific knowledge flows. Combining them together, we obtained a more precise and comprehensive picture of domain evolution and uncover more coherent development pathways between scientific ideas. [ABSTRACT FROM AUTHOR]

Citation based metrics are widely used to assess the impact of research published in journals. This paper presents the results of a research study to verify the accuracy of data and calculations of journal impact metrics presented in Web of Science (WoS) and Scopus in the case of three journals of information and library science. Data collected from the websites of journals were compared with that of two citation extended databases. The study manually calculated the Journal Impact Factor (JIF) and the Impact per Publication (IPP) in accordance with formulas given in the databases. Data were also collected from the Google Scholar to draw a comparison. The study found discrepancies in two sets of data and bibliometric values, i.e., systematic values presented in WoS and Scopus and calculated in this study. Commercial databases presented inflated measures based on fabricated or erroneous data. The study is of practical importance to researchers, universities and research financing bodies that consider these bibliometric indicators as a good tool for measuring performance, assessment, and evaluation of research quality as well as researchers. [ABSTRACT FROM AUTHOR]