Network Society (Spring 2020)

Abstract

The main outcomes of the course are that students are equipped with (1) a basic understanding of concepts of data, networks and data analytics; and (2) a toolbox of techniques and theories to discuss the idea of networks and data as a basis for conceptualizing a network society.

Description

The course is important because most of today’s IT use involves connectivity through global networks, and this makes a basic understanding of such a network society essential for any professional practice in the field of digital design.

We live in world where people from around the globe are only as far away as a flick of the finger on a smartphone touchscreen, where information about anything is at your fingertips and where we often use the word "network" to characterize our infrastructures, our friends and acquaintances and even our societies. As a result, nearly everything we do generates various types of data that are collected, stored and processed by a range of known and unknown entities often with unexpected consequences. In this course, students explore the nature and backgrounds of common characterizations of a "network society" and assess the related assumptions and implications through learning about digital data collection, storage and analytics. What do the terms ‘network’ and ‘network society’ mean? What developments are regarded as characteristic of the ‘network society’? What assumptions about technology development underlie network thinking? What kinds of futures might we expect given our current trajectories?
 
The course aims to provide a "toolbox" of techniques and theories which will enable the student to discuss the basic idea of networks as a basis for the conceptualization of network society. The course is an introduction to big data, network analysis, network theory, and data analytics. The students are introduced to concepts about data and data types, data storage, access and manipulation, and data analytics.
 
Relevant topics in this course include:

• Data types and data sources - measurability, curation, classification and archiving
• Data formats and storage approaches - anonymization, open data, storage systems, archival systems, ephemerality
• Concepts of digital networks and network society
• Social network site data production and publicly available social media data
• Analytics approaches, algorithmic black-boxing, politics of classification
• Ethical, privacy surveillance and security concerns in the digital world

Formal prerequisites

The course builds upon the previous semester. It is a requirement that students have attended Digital Media Analysis.

Intended learning outcomes

After the course, the student should be able to:

• Identify different data types, their sources, function and limitations.
• Differentiate among data analytics approaches and reflect on their potential benefits and pitfalls.
• Reflect on ethical and privacy issues involved in data collection, storage and analysis of various data types and sources.
• Relate data collection, storage and analytics to concepts and theories of the network society.
• Apply methods of digital media analysis to the major themes in the study of network society.

Learning activities

• Lectures by the teachers, guest talks, student presentations and ongoing discussions. 
• Practical / analytical exercises to test and apply the presented theories and concepts during the exercise sessions and after. 
• The students hand in two assignments in groups and will get feedback from lecturers and TAs.

Course literature

The course literature is published in the course page in LearnIT.

Student Activity Budget

Estimated distribution of learning activities for the typical student

• Preparation for lectures and exercises: 25%
• Lectures: 20%
• Exercises: 20%
• Assignments: 10%
• Exam with preparation: 25%

Ordinary exam

Exam type:
C: Submission of written work, external (7-trinsskala)
Exam variation:
C: Submission of written work

Time and date