Knowledge Graphs and Network Text Analysis

A knowledge graph is a kind of semantic network representing some scientific theory. The article describes the present state of this field and addresses a number of problems that have not yet been solved. These problems are implicit relations, strength of (causal) relations, and exclusiveness. Concepts might be too broad or complex to be used properly, so directions for solving these problems are explored. The solutions are applied to a knowledge graph in the field of labour markets

A Network Text Analysis of Fight Club

Network Text Analysis (NTA) involves the creation of networks of words and/or concepts from linguistic data. Its key insight is that the position of words and concepts in a text network provides vital clues to the central and underlying themes of the text as a whole. Recent research has used an inductive or bottom-up approach to the question of theme extraction. In this paper we take a top-down or deductive approach in that we first establish prior expectations as to the key themes to be found in the text. We then compare and contrast the results of our network analysis with the results of literary and cultural analyses of the film Fight Club as reported in over four dozen other peer-reviewed publications. While our results are remarkably consistent with and complementary to results in those studies, our analysis permits something the others do not—an analytical framework for relating those underlying and central themes to one another

Transnational Activism in Support of National Protest: Questions of Identity and Organization

This article considers the question of whether transnational activism supporting national protest attains a cohesive collective identity on social media whilst organizationally remaining localized. It examines a corpus of social media data collected in the course of two months of rolling protests in 2013 against the largest proposed open-cast gold mine at Roşia Montană, Romania, which echoed among Romanian expatriates. A network text analysis of the data supplemented with interview findings revealed concerns with protest logistics as common across the transnational networks of protest localities on both Facebook and Twitter, a finding that testified to the coordinated character of the protests. On the other hand, collective identity emerged as the fruit of attempts to surmount localized protest experiences of geographically disparate but civically-minded social media users

The Neurocognitive Process of Digital Radicalization: A Theoretical Model and Analytical Framework

Recent studies suggest that empathy induced by narrative messages can effectively facilitate persuasion and reduce psychological reactance. Although limited, emerging research on the etiology of radical political behavior has begun to explore the role of narratives in shaping an individual’s beliefs, attitudes, and intentions that culminate in radicalization. The existing studies focus exclusively on the influence of narrative persuasion on an individual, but they overlook the necessity of empathy and that in the absence of empathy, persuasion is not salient. We argue that terrorist organizations are strategic in cultivating empathetic-persuasive messages using audiovisual materials, and disseminating their message within the digital medium. Therefore, in this paper we propose a theoretical model and analytical framework capable of helping us better understand the neurocognitive process of digital radicalization

An Analysis of Research Trends in Brain-based Learning in Adult Education and HRD Fields: The Content Analysis and Network Text Analysis

The purpose of this study is to address the trends of the research on brain-based learning and to present an integrative theoretical framework to provide new insights and future directions in adult education and HRD fields. Based on the neuroscientific perspective, the implications of which the ways to conceptually broaden educational research and practice were discussed

Using visual analytics to make sense of railway Close Calls

In the big data era, large and complex data sets will exceed scientists’ capacity to make sense of them in the traditional way. New approaches in data analysis, supported by computer science, will be necessary to address the problems that emerge with the rise of big data. The analysis of the Close Call database, which is a text-based database for near-miss reporting on the GB railways, provides a test case. The traditional analysis of Close Calls is time consuming and prone to differences in interpretation. This paper investigates the use of visual analytics techniques, based on network text analysis, to conduct data analysis and extract safety knowledge from 500 randomly selected Close Call records relating to worker slips, trips and falls. The results demonstrate a straightforward, yet effective, way to identify hazardous conditions without having to read each report individually. This opens up new ways to perform data analysis in safety science