Применение компьютерной лингвистики для анализа основных направлений исследований в журнале «Экономика региона»

Over the past decades, the process of knowledge generation has accelerated, producing a lot of scientific publications, which makes reviewing even a relatively narrow subject area very demanding, if not impossible. However, recent text data mining tools can assist researchers in conducting such analysis in an objective and time-efficient way. We conduct such a literature review on 1307 articles published in the journal Economy of Regions from 2010 to 2021 using advanced topic modelling techniques. This analysis aims to describe the main research areas in the journal over time, the dynamics of their popularity and the relationship with key quantitative indicators. We identified 22 topics ranging from“Agriculture” and“Economic Geography” to“Fiscal Policy” and“Entrepreneurship”. We estimate how popularity of these topics was changing over time and find topics that gained the most popularity from 2010 to 2021 (+17.61 %,“Spatial Economics”) or lost it (-14.58 %,“Economics of Innovation”). The topic of environmental economics collects the largest number of citations per article (3.64, on average), and the topics on monetary policy and poverty are the most popular among manuscripts in English, which is also true for articles written by authors with foreign affiliation. Papers with third-party funding are concentrated the most in “Spatial Economics” (around 11 %), and the least — in “Agriculture”. Our results can help to understand the evolution in scope of research of Economy of Regions and serve researchers to find promising directions for future studies. © Savin, I. V., Teplyakov, N. S. Text. 2022.Russian Foundation for Basic Research, РФФИ; Russian Science Foundation, RSF: 19–18–00262; Kementerian Riset, Teknologi dan Pendidikan TinggiIt is also worth stressing that the covariates we use do not have high Pearson correlation coefficients, but those coefficients that we present below are significant at the 1 % level. The highest correlation is between year of publication and presence of third-party funding (0.5), which reveals the fact that studies published in ER in recent years have been supported by either Russian (Russian Science Foundation, Russian Foundation for Basic Research) or sometimes also foreign grants (e.g., Ministry of Education and Science of the Republic of Kazakhstan or National Science Centre Poland). Affiliation of one of the co-authors to a foreign university, contrary to our initial expectation, is not so strongly correlated with English language of the article (coefficient is 0.44, which is a moderate value). This can be explained by a considerable share of native Russian-speaking authors who went abroad but continue collaboration with their colleagues in Russia and publish in Russian. Finally, the share of articles with an author affiliated abroad has a slight tendency to increase in ER over time (Pearson correlation with year of publication is 0.12).The article has been prepared with the support from the Russian Science Foundation, conducted as part of the research project № 19–18–00262 “Modelling a balanced technological and socio-economic development of the Russian regions”.We proceed with exploring how papers are distributed by topic depending on whether they are supported by third-party funding or not. In other words, the topics most and least likely to have received third-party funding will be identified. The bottom panel in Figure 7 shows the result. The most supported topic by a wide margin is T13 on spatial economics, accounting for about 11 % of funded manuscripts. For example, the work of Karim et al.1 (belonging to 86.5 % to T13) on studying the spatial effect of transport infrastructure expansion on regional growth was funded by the Ministry of Research, Technology and Higher Education of Republic of Indonesia. The least funded topic is Agriculture (T4), although there is little difference from other less supported topics