In today’s world, the problem of developing methods for the prediction and allocation of promising areas and trends in the innovation technology, e.g., artificial intelligence, research is essential to the scientific community. This article discusses a new scientometric approach towards the identification of trends in highlycited computer technology publications, artificial intelligence publications specifically. The approach in question is based on the bibliographic and thematic analyses of disciplines and impliesthe review of academic profiles in Google Scholar (GS). By reviewing top journals and GS metrics of artificial intelligence articles published in the last five years (2014/2019), a set of key terms was identified that defined the research field the direction. Thefrequency analysis of terms revealed the behavior patterns of the current trendsin artificial intelligence research. The analysis of profiles and highly-cited articles showed that paper citation is influenced by the following factors:appropriate word choices and the frequency of their occurrence in publications and academic profiles. A link was found between the number of keywordsdefining the trend and the number of citations. The study showed that the citation count depends not only on the popularity of the topic but also on the journal’s editorial policies, on the occurrence of hot terms in the paper title, keywords, in the text of an article, and in the academic profile of the author. The trend evaluation algorithm proposed in this study can be applied to research papers in diverse fields to determine their relevance and citation life cycles.
Volume 12 | 02-Special Issue