Digital Methods and Transformation – Applied Social Network Analysis. Summer Course SUMMER 2020

Introduction

Digital technologies are increasingly permeating the way we work, live, and think. This summer school intends to equip students with a set of analysis techniques to understand the antecedents, effects and outcomes of this digital transformation better. The focus will be on social network analysis as a method to study the social and economic implications of digital technology from an empirical point of view. In particular, during two weeks, we encourage students to reflect on the impact of digital technologies on the way we work, may it be through participating in new modes of virtual work, may it be through new forms of crowdworked creativity, or participating in new forms of collaboration that combine elements of work and play. We will try to uncover and find managerial points of action for instance for the sharing economy, to the practice of social media marketing, to new forms of algorithmic management, to emerging business models in the digital economy, and to other new forms of working.

Our search for solutions will be underpinned by learning about social network methods. Social network analysis is interested in the relational properties of organizations and individuals. While the method has been developed in the pre-digital era for small-scale data, it is especially suited for user-generated trace data that contains relational elements. By using social network analysis, communities and sub-communities can be identified and clustered based on core attributes. Moreover, social network analysis is a key method to identify influentials or important and noteworthy elements in a network. Thus, social network analysis is a versatile and widely used method with many benefits, especially in times of big data and user-generated data from social and digital media. A solid foundation in social network analysis and adjacent methods will provide the students not only with a toolset to analyze communities effectively but also with a relational way of thinking through core concepts of the method.  

Course content

1. Introduction: Why Social Networks? 
   An overview of the social network analysis approach, showcasing current examples in the form of interesting research and company studies. Students will get a first grasp of practical questions and challenges related to new forms of production and work that come with digital technologies.

2. Principles of Social Network Analysis I 
   The scientific origins of social network analysis, introducing some fundamental concepts from graph theory. Introduction of concepts such as ego-, group-, and global networks, and their applicability to real-world challenges.

3. Principles of Social Network Analysis II 
   More core concepts of social network analysis, including network centrality and network mechanisms

4. Practice-Oriented Social Network Analysis 
   Introduction to social network analysis software such as Gephi through hands-on exercices

5. Essentials of Data Collection and Data Management
   Sources for gathering social network data, from APIs, to repositories and online communities. Introduction to Python for web scraping and to SQL for data management

6. Visual Analysis 
   Visual display of data in general and network visualization in particular

7. User-Focused Analysis of Social Media and Social Network Data
   Introduction to design thinking and the persona concept for summarizing, spotlighting and communcating data

8. Project Presentation and Discussions 
   The students will present their findings to the group and discuss them critically with the other participants of the course. The main findings, implications and challenges during the research project are addressed, trying to condense the key learning across the groups.