IS40980 Social Networks Online and Offline

Academic Year 2021/2022

IS40980 - Spatialising Social Media introduces students to concepts, theories, and methods from social network analysis and their application to online and offline social networks. The module covers the rationale of social network analysis which states that relationships, more than individual and independent attributes, are critical to understanding social behaviour. The course is structured around the differences and similarities observed in online (e.g., social media activity) and physical social networks (such as family and friend relationships). Students will be introduced to a range of networks, including friendship networks, political discussion networks, social support networks, organizational networks, and online social networks.

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Curricular information is subject to change

Learning Outcomes:

Module aims:
• To introduce students to graph theory and social network analysis
• To explore and visualize social variables that can be defined in terms of relationships as opposed to independent attributes
• To provide an overview of physical social networks and their characteristics such as homophily, small-world properties, and clustering
• Similarly, to provide an overview of the characteristics of online social networks such as scale-free distributions, polarization, algorithmic ranking, and echo-chamber communication
• To provide an introduction to social network analysis tools and software, such as statnet, igraph, Gephi, and UCINET
• To develop an understanding of how and under which circumstances online social networks can be mapped onto offline social networks

Knowledge and understanding:
On successful completion of this module, you will be expected to be able to:
• Describe the fundamentals of graph theory and explain the role of relationships in studying social behaviour
• Be familiar with seminal work in social network analysis and its applications
• Have the capacity to explain how different network layouts can be transformed or converted into one another, e.g., edge lists, matrices, tables, lattices
• Be able to describe and debate key issues cutting across online and offline social networks
• Evaluate the limitations and challenges involved in mapping online to offline social networks

Learning outcomes:
Upon completing the module, you will be expected to be able to:
• Discuss and debate current issues of social network analysis and social media
• Demonstrate clear written communication, oral communication, and presentation skills
• Present, evaluate, and interpret relational data in connection to communication, sociological, and spatial theories
• Make reasoned judgements and demonstrate a capacity for independent thinking
• Access and utilise research resources drawing from social network analysis, sociology, communication, and spatial statistics in relation to social media and offline social networks
• Critically describe the relationships between online and offline social networks, and how these sources of social activity can be mapped onto each other
• Be familiar with methods to collect, retrieve, visualise, and analyse social network data online (e.g. social media platforms) and offline (e.g. institutional or intergroup affiliation)
• Undertake accurate reading and clear written communication
• Show self-reliance and the ability to manage time and work to strict deadlines
• Evaluate complex arguments to critically assess practice and procedure

Indicative Module Content:

Week 01: Introduction to Social Network Data
Week 02: Introduction to Social Network Analysis
Week 03: Measures of Centrality
Week 04: Homophily and Communities
Week 05: Diffusion and Contagion
Week 06: Local and Digital
Week 07: Social and Spatial Networks
Week 08: Readership Online and Offline
Week 09: Physical and Online Networks
Week 10: Mapping Online to Offline Networks
Week 11: Protest Coordination Online and Offline
Week 12: Mapping Social Data

Student Effort Hours: 
Student Effort Type Hours




Approaches to Teaching and Learning:
The module employs a combination of lecture and tutorial (seminar) in the computer lab. The module takes a non-mathematical approach to social networks, but students will benefit from having been introduced to graph theory and computer routines for analysis and visualization of social networks.

Requirements, Exclusions and Recommendations
Learning Requirements:

• Background in sociology or social sciences, including anthropology, communication, economics, geography, information sciences, linguistics, political science, and psychology
• Familiarity with algorithms and computational social sciences
• Knowledge of graphs and familiarity with probability distribution and random variables
• Familiarity with social media platforms such as Facebook, Twitter, and Instagram

Module Requisites and Incompatibles
Not applicable to this module.
Assessment Strategy  
Description Timing Open Book Exam Component Scale Must Pass Component % of Final Grade
Essay: Students may choose to be assessed on the basis of one 3000-word piece of written coursework (excluding diagrams, graphs, images, or bibliography). Students may go over or under the limit by 10%. Coursework (End of Trimester) n/a Graded No


Portfolio: Students may choose to write 300-word reaction papers addressing the weekly core readings. Throughout the Trimester n/a Graded No


Carry forward of passed components
Resit In Terminal Exam
Summer No
Please see Student Jargon Buster for more information about remediation types and timing. 
Feedback Strategy/Strategies

• Feedback individually to students, on an activity or draft prior to summative assessment
• Feedback individually to students, post-assessment
• Group/class feedback, post-assessment

How will my Feedback be Delivered?

Not yet recorded.

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Name Role
Mr Andrew Woods Lecturer / Co-Lecturer