GRA 6847 Digital Methods and Transformation – Applied Social Network Analysis. Summer Course- CANCELLED SUMMER 2020

GRA 6847 Digital Methods and Transformation – Applied Social Network Analysis. Summer Course- CANCELLED SUMMER 2020

Course code: 
GRA 6847
Department: 
Communication and Culture
Credits: 
6
Course coordinator: 
Christian Fieseler
Christoph Lutz
Product category: 
Master
Portfolio: 
MSc Summer Courses
Semester: 
2020 Spring
Active status: 
Hold - temporarily
Deactivate term: 
2020 Spring
Teaching language: 
English
Course type: 
One semester
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.  

Learning outcomes - Knowledge

After taking the summer school, the students should have acquired knowledge of:

  • relational dynamics of social and digital media
  • data sources that feed into the analysis of social networks
  • network visualizations and key metrics that describe the structure of a social network
  • web scraping and the fundamentals of data management
  • persona development and fundamental principles of design thinking
Learning outcomes - Skills

After taking the summer school, students should be able to:

  • collect social media data via APIs
  • apply basic web scraping for simple websites and online communities with Python
  • conduct social network analysis on a range of data using software such as Gephi or R
  • perform network visualization using Gephi and general visualization using Tableau
  • design stakeholder personas based on user-driven methodologies such as design thinking
General Competence

After taking the summer school, students should:

  • have developed a critical understanding of the managerial and social challenges of digital transformation
  • be able to interpret the dynamic role of influence in social networks as expressed in phenomena like influentials and opinion leaders
  • have reflected on the role of data, both big data and small data, and its role in transforming work and society
  • have interrogated key concepts learned in the course such as social capital and social networks
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.
Teaching and learning activities

Students should have taken classes in statistics and have working knowledge of MS Excel or SPSS. We expect students to have a solid grasp of the English language as well as a strong interest in the issues at hand, and to actively participate in class.

Please note that while attendance is not compulsory in all courses, it is the student’s own responsibility to obtain any information provided in class that is not included on the course homepage/itslearning or text book.

This is a course with continuous assessment (several exam components) and one final exam code. Each exam component is graded by using points on a scale from 0-100. The components will be weighted together according to the information in the course description in order to calculate the final letter grade for the examination code (course). Students who fail to participate in one/some/all exam elements will get a lower grade or may fail the course. You will find detailed information about the point system and the cut off points with reference to the letter grades when the course starts.

At resit, all exam components must, as a main rule, be retaken during next scheduled course.

Computer-based tools: Gephi, Python, Tableau, SQL, potentially R

Software tools
Gephi
R
Tableu
Qualifications

All courses in the Masters programme will assume that students have fulfilled the admission requirements for the programme. In addition, courses in second, third and/or fourth semester can have specific prerequisites and will assume that students have followed normal study progression. For double degree and exchange students, please note that equivalent courses are accepted.

Exam categoryWeightInvigilationDurationGroupingComment exam
Exam category:
Activity
Form of assessment:
Presentation
Exam code:
GRA 68471
Grading scale:
Point scale
Grading rules:
Internal examiner
Resit:
All components must, as a main rule, be retaken during next scheduled course
30No15 Minute(s)Group ( 1 - 4)Students are expected to produce and perform a poster presentation on a digital transformation challenge, based on insights gained through social network analysis. To create this poster, students will gather primary data on their chosen topic, relying on the approaches outlined in class. Group size may vary depending on class size.
Exam category:
Submission
Form of assessment:
Written submission
Exam code:
GRA 68471
Grading scale:
Point scale
Grading rules:
Internal and external examiner
Resit:
All components must, as a main rule, be retaken during next scheduled course
70No 3 Week(s)Group ( 1 - 4)Students are expected to document their data gathering and analysis approach in a methods paper (no longer than 15 pages). Group size may vary depending on class size.
Exams:
Exam category:Activity
Form of assessment:Presentation
Weight:30
Invigilation:No
Grouping (size):Group (1-4)
Duration:15 Minute(s)
Comment:Students are expected to produce and perform a poster presentation on a digital transformation challenge, based on insights gained through social network analysis. To create this poster, students will gather primary data on their chosen topic, relying on the approaches outlined in class. Group size may vary depending on class size.
Exam code:GRA 68471
Grading scale:Point scale
Resit:All components must, as a main rule, be retaken during next scheduled course
Exam category:Submission
Form of assessment:Written submission
Weight:70
Invigilation:No
Grouping (size):Group (1-4)
Duration: 3 Week(s)
Comment:Students are expected to document their data gathering and analysis approach in a methods paper (no longer than 15 pages). Group size may vary depending on class size.
Exam code:GRA 68471
Grading scale:Point scale
Resit:All components must, as a main rule, be retaken during next scheduled course
Type of Assessment: 
Continuous assessment
Grading scale: 
ECTS
Total weight: 
100
Sum workload: 
0

A course of 1 ECTS credit corresponds to a workload of 26-30 hours. Therefore a course of 6 ECTS credits corresponds to a workload of at least 160 hours.