scholarly journals #Dark inspiration: Eudaimonic entertainment in extremist Instagram posts

2020 ◽  
pp. 146144481989962
Author(s):  
Lena Frischlich
Keyword(s):  
Cluster Analysis ◽  
Content Analysis ◽  
Prosocial Behavior ◽  
Hierarchical Cluster Analysis ◽  
Qualitative Content Analysis ◽  
Hierarchical Cluster ◽  
Digital Data ◽  
Mixed Methods Case Study ◽  
Current Article

Eudaimonic entertainment, which motivates a reflection on topics such as virtue or meaning, has many benefits, such as fostering wellbeing and inspiring prosocial behavior. Yet, it may also have a darker side when Islamic extremists use accordant elements in online propaganda. So far, this “dark inspiration” has attracted little scholarly interest. The current article fills this gap via a mixed-methods case study of an Islamic extremist influencer on Instagram. The study combined a qualitative content analysis of the account’s postings from 2016 to 2018 ( n = 301 posts), with a hierarchical cluster analysis and digital data on aggregated user response to these posts. I found four types of post, ranging from calls for conservativism to calls for violent jihad. Different eudaimonic cues were used in all four types. Likes and comments varied as a function of type, with the violence promoting posts motivating the largest number of user responses.

scholarly journals Peripheral areas and their distinctive characteristics: The case of Hungary

2021 ◽  
Vol 29 (3) ◽  
pp. 217-230
Author(s):  
János Pénzes ◽  
Gábor Demeter
Keyword(s):  
Cluster Analysis ◽  
Hierarchical Cluster Analysis ◽  
Hierarchical Cluster ◽  
Multivariate Statistical Methods ◽  
Multivariate Statistical ◽  
Development Policies ◽  
Relevant Variables ◽  
Peripheral Areas

Abstract The delimitation and classification of peripheral settlements using multivariate statistical methods is presented in this article, with a case study of Hungary. A combination of four different methods provided the basis for the delimitation of settlements defined as peripheral. As significant overlapping was detected between the results of the different methods, peripheries – more than one-fifth of the Hungarian settlements – were identified in a common set of the results. The independence of the results from the applied methods points to the fact that peripherisation is multi-faceted, and the peripheries of Hungary are stable and well-discernible from other regions. After the identification of peripheral areas, we classified these settlements into groups based on their specific features. Multiple steps specifying the relevant variables resulted in selecting the most appropriate 10 indicators and these served as the basis for a hierarchical cluster analysis, through which 7 clusters (types of peripheries) were identified. Five of them comprised enough cases to detect the most important dimensions and specific features of the backwardness of these groups. These clusters demonstrated a spatial pattern and their socioeconomic and infrastructural features highlighted considerable disparities. These differences should be taken into consideration when development policies are applied at regional levels or below.

scholarly journals What do they TEL(L)? A systematic analysis of master programs in technology-enhanced learning

2022 ◽  
Vol 19 (1) ◽  
Author(s):  
Mikhail Fominykh ◽  
Joshua Weidlich ◽  
Marco Kalz ◽  
Ingunn Dahler Hybertsen
Keyword(s):  
Cluster Analysis ◽  
Hierarchical Cluster Analysis ◽  
Qualitative Content Analysis ◽  
Hierarchical Cluster ◽  
Technology Enhanced Learning ◽  
Systematic Analysis ◽  
Study Programs ◽  
Interdisciplinary Study ◽  
Enhanced Learning ◽  
Scientific Fields

AbstractThis article contributes to the debate on the growing number of interdisciplinary study programs in learning and technology, and aims to understand the diversity of programs as well as curricula structure in an international landscape. Scientific fields share their knowledge and recruit young researchers by offering discipline-specific study programs. Thus, study programs are a reflection of the fields they represent. As technology-enhanced learning is considered to be particularly interdisciplinary and heterogenous, it is important to better understand the landscape of study programs that represents the field. This article presents an analysis of master programs in technology-enhanced learning. A systematic review and analysis of master programs offered in English has been conducted and further used as input for hierarchical cluster analysis. The study identified general characteristics, curricula structure, and organization of topics of these programs. Hierarchical cluster analysis and qualitative content analysis helped us to identify the major types of curricular structures and typical topics covered by the courses. Results show that most study programs rely on interdisciplinary subjects in technology-enhanced learning with a considerable number of subjects from education, learning and psychology. Subjects related to technology, information and computer science appear in such programs less frequently.

scholarly journals Assessment of Sustainable Socioeconomic Development in European Union Countries

2020 ◽  
Vol 12 (5) ◽  
pp. 1986 ◽  
Author(s):  
Viktorija Skvarciany ◽  
Daiva Jurevičienė ◽  
Gintarė Volskytė
Keyword(s):  
Cluster Analysis ◽  
Hierarchical Cluster Analysis ◽  
Socioeconomic Development ◽  
Scientific Literature ◽  
Hierarchical Cluster ◽  
The Third ◽  
Factors Influencing ◽  
European Union Countries ◽  
Current Article ◽  
The Eu

There are still debates in the scientific literature about the factors influencing countries’ sustainable socioeconomic development. Therefore, the current article aims at determining the factors of sustainable socioeconomic development and assessing its level in the EU countries. The following methods were employed for the research: an evaluation based on distance from average solution (EDAS) and hierarchical cluster analysis (HCA). EDAS was used to reveal which countries have the highest level of sustainable socioeconomic development, and which have the lowest. The ranking was done based on the appraisal score, which is an outcome of EDAS. Hierarchical cluster analysis (HCA) was used for clustering the countries based on the appraisal scores in order to distinguish groups of countries having a similar level of sustainable socioeconomic development. The results revealed that the highest level of sustainable socioeconomic development is in Germany, and the lowest in Portugal. Based on HCA, the countries were divided into three groups. The first cluster’s countries have the weakest sustainable socioeconomic development, and countries assigned to the third cluster have the best. In the current research, the third cluster consists of one country, Germany, which supports the results obtained with the EDAS method, i.e., Germany is the country with the highest level of sustainable socioeconomic development in the EU.

Sign in / Sign up

Export Citation Format

Share Document