The Topics Dynamics in Knowledge Management Research
The intellectual structure of academic discipline can be viewed as a set of interacting topics evolving over time. Dynamics of those topics i.e. changes in their popularity and impact is the subject of special attention because it reflects a shift in actual researchers’ interest. This paper analyzes topics of knowledge management (KM) on the base of the topic modeling technique (namely Latent Dirichlet Allocation). Studying the flow of academic publications in 7 leading journals in 2010–2018, we identified 8 topics that concern different aspects of knowledge management science. Three topics, what focus on the social aspects of knowledge management (namely the context supporting knowledge transfer, the employees’ incentives to share knowledge, and innovation), grow in terms of popularity and impact. Opposite, popularity and impact of topics, which focus on the practice of the knowledge management and organizational learning also as on the impact of intellectual capital on performance, decline. It is consistent with the opinion of other researchers that in the contemporary flow of scientific publication role of KM is identified more as a social process than a management engineering method.
Online petitions are usually regarded as one of the most popular channels to involve citizens in the political process. In our paper we have analyzed texts and voting data (pro and against) from 9705 e-petitions submitted from 2013 until 2017 at Russian Public Initiative project. Analysis of dynamics showed stabilization of interest to this resource (emergence of a new authors, growth of “strong” petitions etc.). Studying success factors of electronic petitions at the Russian public initiative project we found out that the topic and lexical information are significant factors, as well as the level of petitions.
Highly cited scientific papers by Russian authors are studied. A definition of highly cited papers based on the interpretation provided by the Essential Science Indicators database is presented; the number of highly cited Russian papers is analyzed against the background of global indices and the disciplinary distribution of these papers is explored. It is shown that in all scientific areas the share of Russian papers that become highly cited is below world average. The impact of coauthorship with foreign scientists on the creation of highly cited papers is investigated. It is concluded that international collaboration has a key role in the related process.
The paper contains a review of the on-line services’ contemporary state, which are providing access to scientific data bases of patents and publications in magazines. On the example of a family of screen’s technologies (CRT, LCD, PDP) were shown the development and the replacement of technological trends in that branch of researches. This analysis was performed on the base of time series of the patent data via methods of the data mining.
This article analyzes the effects of publication language on the international scientific visibility of Russia using the Web of Science (WoS). Like other developing and transition countries, it is subject to a growing pressure to “internationalize” its scientific activities, which primarily means a shift to English as a language of scientific communication. But to what extent does the transition to English improve the impact of research? The case of Russia is of interest in this respect as the existence of many combinations of national journals and languages of publications (namely, Russian and English, including translated journals) provide a kind of natural I experiment to test the effects of language and publisher's country on the international visibility of research through citations as well as on the referencing practices of authors. Our analysis points to the conclusion that the production of original English-language papers in foreign journals is a more efficient strategy of internationalization than the mere translation of domestic journals. If the objective of a country is to maximize the international visibility of its scientific work, then the efforts should go into the promotion of publication in reputed English-language journals to profit from the added effect provided by the Matthew effect of these venues.
The goal of the conference is to help build cross-disciplinary networks of analysts, software specialists, and researchers to advance the use of textual information in multiple science, technology, and business development fields. Within this context, conference themes will include, but are not limited to:
DataSourcing, preparing, and interpreting data sources including patents, publications, webscraping, and other novel data sources
Text-mining tools and methodsBest practices in software-based topic modeling, clumping, association rules, term manipulation, text manipulation, etc. Visualization
Applied researchFuture-Oriented Technology Analysis (FTA) Intelligence gathering to support decision-making in the private sector (e.g., Management of Technology)
In the current context of the globalization of science, excellence is most often associated with internationalization and assessed through high-impact “international” (English-language) publications. Taking Russian economic science as a case study, this paper argues that the strategies of internationalization of national disciplinary fields are primarily determined by the parameters of the global economics itself. My analysis of the Russian publications in economics covered by Web of Science demonstrates that the very repertoire of international publication strategies of Russian authors is determined by the transnational system of communication in economics. Economics papers from peripheral nations are essentially assigned to regional or “area studies” periodicals, which do not belong to the core of the discipline. Publication in top economics journals requires a specific “international” competency usually obtained through doctoral training at Anglo-American or equivalent PhD programs and generally implies a delocalization of research objects and questions.
An important text mining problem is to find, in a large collection of texts, documents related to specic topics and then discern further structure among the found texts. This problem is especially important for social sciences, where the purpose is to nd the most representative documents for subsequent qualitative interpretation. To solve this problem, we propose an interval semi-supervised LDA approach, in which certain predened sets of keywords (that dene the topics researchers are interested in) are restricted to specic intervals of topic assignments. We present a case study on a Russian LiveJournal dataset aimed at ethnicity discourse analysis.
An important text mining problem is to find, in a large collection of texts, documents related to specific topics and then discern further structure among the found texts. This problem is especially important for social sciences, where the purpose is to find the most representative documents for subsequent qualitative interpretation. To solve this problem, we propose an interval semi-supervised LDA approach, in which certain predefined sets of keywords (that define the topics researchers are interested in) are restricted to specific intervals of topic assignments.