scholarly journals From Hermeneutics to Data to Networks: Data Extraction and Network Visualization of Historical Sources

2015 ◽  
Author(s):  
Marten Düring
Keyword(s):  
Social Network ◽  
Data Analysis ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Qualitative Data ◽  
Data Extraction ◽  
Network Visualization ◽  
Network Data ◽  
Qualitative Data Analysis ◽  
Historical Sources

Network visualizations can help humanities scholars reveal hidden and complex patterns and structures in textual sources. This tutorial explains how to extract network data (people, institutions, places, etc) from historical sources through the use of non-technical methods developed in Qualitative Data Analysis (QDA) and Social Network Analysis (SNA), and how to visualize this data with the platform-independent and particularly easy-to-use Palladio.

scholarly journals De la hermenéutica a las redes de datos: Extracción de datos y visualización de redes en fuentes históricas

2017 ◽  
Author(s):  
Marten Düring
Keyword(s):  
Social Network ◽  
Data Analysis ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Qualitative Data ◽  
Redes Sociales ◽  
Qualitative Data Analysis

La visualizaciones de redes pueden ayudar a los humanistas a revelar patrones complejos escondidos y estructuras en fuentes textuales. Este tutorial explica cómo extraer datos en red (personas, instituciones, lugares, etcétera.) de fuentes históricas a través del uso de métodos no especializados desarrollados en el marco del análisis de datos qualitativos (Qualitative Data Analysis, QDA) y el análisis de redes sociales (Social Network Analysis, SNA), y cómo visualizar estos datos con Palladio, una aplicación independiente de plataforma y que es particularmente fácil de usar.

Duality beyondPersons and Groups

2020 ◽  
pp. 391-413
Author(s):  
Sophie Mützel ◽  
Ronald Breiger
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Network Data ◽  
Future Directions ◽  
Affiliation Networks ◽  
The Social ◽  
Recent Developments ◽  
Principle Of Duality ◽  
Seminal Article

This chapter focuses on the general principle of duality, which was originally introduced by Simmel as the intersection of social circles. In a seminal article, Breiger formalized Simmel’s idea, showing how two-mode types of network data can be transformed into one-mode networks. This formal translation proved to be fundamental for social network analysis, which no longer needed data on who interacted with whom but could work with other types of data. In turn, it also proved fundamental for the analysis of how the social is structured in general, as many relations are dual (e.g. persons and groups, authors and articles, organizations and practices), and are thus susceptible to an analysis according to duality principles. The chapter locates the concept of duality within past and present sociology. It also discusses the use of duality in the analysis of culture as well as in affiliation networks. It closes with recent developments and future directions.

scholarly journals Scientific collaboration network ammong Brazilian universities

2013 ◽  
Vol 6 (1) ◽  
Author(s):  
Maria Isabel Escalona-Fernandez ◽  
Antonio Pulgarin-Guerrero ◽  
Ely Francina Tannuri de Oliveira ◽  
Maria Cláudia Cabrini Gracio
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Scientific Collaboration ◽  
Methodological Approach ◽  
Scientific Production ◽  
Collaboration Network ◽  
Network Data ◽  
New Knowledge

This paper analyses the scientific collaboration network formed by the Brazilian universities that investigate in dentistry area. The constructed network is based on the published documents in the Scopus (Elsevier) database covering a period of 10 (ten) years. It is used social network analysis as the best methodological approach to visualize the capacity for collaboration, dissemination and transmission of new knowledge among universities. Cohesion and density of the collaboration network is analyzed, as well as the centrality of the universities as key-actors and the occurrence of subgroups within the network. Data were analyzed using the software UCINET and NetDraw. The number of documents published by each university was used as an indicator of its scientific production.

Social Network Analysis

2020 ◽  
pp. 107-117
Author(s):  
Sheik Abdullah A. ◽  
Abiramie Shree T. G. R.
Keyword(s):  
Social Network ◽  
Data Analysis ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Data Storage ◽  
Social Data ◽  
Cloud Data ◽  
Huge Data ◽  
Cloud Data Storage ◽  
Social Data Analysis

Each day, 2.5 quintillion bytes of data are generated due to our daily activity. It is due to the vast amount of use of the smart mobiles, Cloud data storage, and the Internet of Things. In earlier days, these technologies were utilized by large IT companies and the private sector, but now each person has a high-end smartphone along with the cloud and IoT for the easy storage of data and backup. The analysis of the data generated by social media is a tedious process and involves a lot of techniques. Some tools for social network analysis are: Gephi, Networkx, IGraph, Pajek, Node XL, and cytoscope. Apart from these tools there are various efficient social data analysis algorithms that are far more helpful in doing analytics. The need for and use of social network analysis is very helpful in our current problem of huge data generation. In this chapter, the need for the analysis of social data along with the tools that are needed for the analysis and the techniques that are to be implemented in the field of social data analysis are covered.

Social Network Analysis in Marketing

E-Marketing ◽  
2012 ◽  
pp. 185-197
Author(s):  
Przemyslaw Kazienko ◽  
Piotr Doskocz ◽  
Tomasz Kajdanowicz
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Social Relationships ◽  
Network Data ◽  
Social Network Data ◽  
The Social ◽  
Targeted Marketing ◽  
Potential Customers ◽  
Demographic Features

The chapter describes a method how to perform a classification task without any demographic features and based only on the social network data. The concept of such collective classification facilitates to identify potential customers by means of services used or products purchased by the current customers, i.e. classes they belong to as well as using social relationships between the known and potential customers. As a result, a personalized offer can be prepared for the new clients. This innovative marketing method can boost targeted marketing campaigns.

Social Network Analysis in Marketing

2011 ◽  
pp. 271-283
Author(s):  
Przemyslaw Kazienko ◽  
Piotr Doskocz ◽  
Tomasz Kajdanowicz
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Social Relationships ◽  
Network Data ◽  
Social Network Data ◽  
The Social ◽  
Targeted Marketing ◽  
Potential Customers ◽  
Demographic Features

The chapter describes a method how to perform a classification task without any demographic features and based only on the social network data. The concept of such collective classification facilitates to identify potential customers by means of services used or products purchased by the current customers, i.e. classes they belong to as well as using social relationships between the known and potential customers. As a result, a personalized offer can be prepared for the new clients. This innovative marketing method can boost targeted marketing campaigns.

Using Social Network Analysis (SNA) to Study Members and Activists of Political Parties

2020 ◽  
Vol 147-148 (1-2) ◽  
pp. 13-48
Author(s):  
Emilien Paulis
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Political Parties ◽  
Political Party ◽  
Methodological Approach ◽  
Network Data ◽  
Party Member ◽  
Party Membership ◽  
Local Party

This article explores the development of my PhD dissertation’s methodological approach, based on Social Network Analysis (SNA), or the collection and analysis of network data, in order to deal with political parties and their members (party membership). I extensively relied on this alternative, growing methodological background in three extents. First (1), SNA was used to analyze bibliographic references related to my dissertation topic, i.e. party membership studies, and identify the most central authors, thereby illustrating the literature review while describing their key contributions. Second (2), SNA was employed to collect and analyze network data likely to better grasp how interpersonal networks affect the probability for a random citizen to turn into party member, assuming that social influence matters in the process of joining a political party. Third (3), I further capitalized on SNA to deal with the question of party activism and why some members become active whereas others remain passive, arguing theoretically and showing empirically that part of the answer lies in members’ position within their local party branch’s social network. Each of these three applications is discussed in the light of the main methodological developments, the empirical findings and their interpretation, while shortcomings and research opportunities are more systematically highlighted at the end.

Sign in / Sign up

Export Citation Format

Share Document