The Methodological Foundations of Standardization in the Field of Library and Information Support of Science

This paper characterizes the methodological foundations of national standardization in the Russian Federation, in particular in the library and information field. The activities of Technical Standardization Committee 191 Scientific and Technical Information, Librarianship, and Publishing are reported.     

The Blockchain Technology in Scientific and Information Activities

The basic properties of the blockchain technology are discussed to allow informed decisions on its selection and use in scientific and information activities. Specific examples are given and the efficiency of this technology is analyzed.     

The Information Nature of Culture

This paper explores the information nature of culture. It is represented as an extragenetic and nonorganizational information process and system. Information is regarded as a universal property of matter that is essentially associated with development processes and realizes various forms and ways of storage and circulation at various development stages of global evolution. The socio-cultural principle of exogenous accumulation, transmission, and transformation of information is analyzed in the context of the continuing growth of the information content of material systems on the superhighway of global-universal evolution. It is noted that the information concept of culture evolves into a new area of cultural studies, that is information and cultural studies, advancing our understanding of the nature of culture and cultural genesis.     

Retrieving and classifying instances of source code plagiarism

Automatic detection of source code plagiarism is an important research field for both the commercial software industry and within the research community. Existing methods of plagiarism detection primarily involve exhaustive pairwise document comparison, which does not scale well for large software collections. To achieve scalability, we approach the problem from an information retrieval (IR) perspective. We retrieve a ranked list of candidate documents in response to a pseudo-query representation constructed from each source code document in the collection. The challenge in source code document retrieval is that the standard bag-of-words (BoW) representation model for such documents is likely to result in many false positives being retrieved, because of the use of identical programming language specific constructs and keywords. To address this problem, we make use of an abstract syntax tree (AST) representation of the source code documents. While the IR approach is efficient, it is essentially unsupervised in nature. To further improve its effectiveness, we apply a supervised classifier (pre-trained with features extracted from sample plagiarized source code pairs) on the top ranked retrieved documents. We report experiments on the SOCO-2014 dataset comprising 12K Java source files with almost 1M lines of code. Our experiments confirm that the AST based approach produces significantly better retrieval effectiveness than a standard BoW representation, i.e., the AST based approach is able to identify a higher number of plagiarized source code documents at top ranks in response to a query source code document. The supervised classifier, trained on features extracted from sample plagiarized source code pairs, is shown to effectively filter and thus further improve the ranked list of retrieved candidate plagiarized documents.     

Applying LDA Topic Modeling in Communication Research: Toward a Valid and Reliable Methodology

ABSTRACT

Latent Dirichlet allocation (LDA) topic models are increasingly being used in communication research. Yet, questions regarding reliability and validity of the approach have received little attention thus far. In applying LDA to textual data, researchers need to tackle at least four major challenges that affect these criteria: (a) appropriate pre-processing of the text collection; (b) adequate selection of model parameters, including the number of topics to be generated; (c) evaluation of the model’s reliability; and (d) the process of validly interpreting the resulting topics. We review the research literature dealing with these questions and propose a methodology that approaches these challenges. Our overall goal is to make LDA topic modeling more accessible to communication researchers and to ensure compliance with disciplinary standards. Consequently, we develop a brief hands-on user guide for applying LDA topic modeling. We demonstrate the value of our approach with empirical data from an ongoing research project.     

Scaling up Content Analysis

ABSTRACT

Employing a number of different standalone programs is a prevalent approach among communication scholars who use computational methods to analyze media content. For instance, a researcher might use a specific program or a paid service to scrape some content from the Web, then use another program to process the resulting data, and finally conduct statistical analysis or produce some visualizations in yet another program. This makes it hard to build reproducible workflows, and even harder to build on the work of earlier studies. To improve this situation, we propose and discuss four criteria that a framework for automated content analysis should fulfill: scalability, free and open source, adaptability, and accessibility via multiple interfaces. We also describe how to put these considerations into practice, discuss their feasibility, and point toward future developments.     

Granularity of algorithmically constructed publication-level classifications of research publications: Identification of topics

The purpose of this study is to find a theoretically grounded, practically applicable and useful granularity level of an algorithmically constructed publication-level classification of research publications (ACPLC). The level addressed is the level of research topics. The methodology we propose uses synthesis papers and their reference articles to construct a baseline classification. A dataset of about 31 million publications, and their mutual citations relations, is used to obtain several ACPLCs of different granularity. Each ACPLC is compared to the baseline classification and the best performing ACPLC is identified. The results of two case studies show that the topics of the cases are closely associated with different classes of the identified ACPLC, and that these classes tend to treat only one topic. Further, the class size variation is moderate, and only a small proportion of the publications belong to very small classes. For these reasons, we conclude that the proposed methodology is suitable to determine the topic granularity level of an ACPLC and that the ACPLC identified by this methodology is useful for bibliometric analyses.     

Google Scholar and Web of Science: Examining gender differences in citation coverage across five scientific disciplines

Many studies demonstrate differences in the coverage of citing publications in Google Scholar (GS) and Web of Science (WoS). Here, we examine to what extent citation data from the two databases reflect the scholarly impact of women and men differently. Our conjecture is that WoS carries an indirect gender bias in its selection criteria for citation sources that GS avoids due to criteria that are more inclusive. Using a sample of 1250 U.S. researchers in Sociology, Political Science, Economics, Cardiology and Chemistry, we examine gender differences in the average citation coverage of the two databases. We also calculate database-specific h-indices for all authors in the sample. In repeated simulations of hiring scenarios, we use these indices to examine whether women's appointment rates increase if hiring decisions rely on data from GS in lieu of WoS. We find no systematic gender differences in the citation coverage of the two databases. Further, our results indicate marginal to non-existing effects of database selection on women's success-rates in the simulations. In line with the existing literature, we find the citation coverage in WoS to be largest in Cardiology and Chemistry and smallest in Political Science and Sociology. The concordance between author-based h-indices measured by GS and WoS is largest for Chemistry followed by Cardiology, Political Science, Sociology and Economics.     

Placing Facebook

Facebook is challenging professional journalism. These challenges were evident in three incidents from 2016: the allegation that Facebook privileged progressive-leaning news on its trending feature; Facebook’s removal of the Pulitzer Prize-winning “Napalm Girl” photo from the pages of prominent users; and the proliferation of “fake news” during the US presidential election. Using theoretical concepts from the field of boundary work, this paper examines how The Guardian, The New York Times, Columbia Journalism Review and Poynter editorialized Facebook’s role in these three incidents to discursively construct the boundary between the value of professional journalism to democracy and Facebook’s ascendant role in facilitating essential democratic functions. Findings reveal that these publications attempted to define Facebook as a news organization (i.e., include it within the boundaries of journalism) so that they could then criticize the company for not following duties traditionally incumbent upon news organizations (i.e., place it outside the boundaries of journalism). This paper advances scholarship that focuses on both inward and outward conceptions of boundary work, further explores the complex challenge of defining who a journalist is in the face of rapidly changing technological norms, and advances scholarship in the field of media ethics that positions ethical analysis at the institutional level.