IS41510 Social Networks Online and Off

Academic Year 2023/2024

Social Networks Online and Offline: Spatializing 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. The module draws from Dr Bastos’ research mapping online social networks to geographically situated communities (Spatializing Social Media: Social Networks Online and Offline, Routledge: 2021).

• 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

Values and attitudes:
• Students are expected to show respect for colleagues attending the module who might lack familiarity with social network analysis.
• Students are expected to correctly reference scholarship and display uncompromising adherence to good academic practice.

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.

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Curricular information is subject to change

Learning Outcomes:

The course aims are:
• 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:
Weekly core readings are mandatory and comprise 4 short papers or book chapters. The core readings are available on Brightspace for your convenience. These readings are available for you only and must not be shared outside this module.

In addition to the core readings, a set of recommended readings further explores the theoretical questions covered in the weekly class. The recommended readings are available in the library and online. We encourage you to post your questions and answers to the forum and/or the message board and to comment on each other’s answers.

Teaching provision:
The module is taught face-to-face. Please make sure to bring your own laptop to class. You should install Gephi and R to complete the in-class exercises and the optional assignments. I also recommend installing RStudio, which makes it easier to work with R.

Both R and Gephi are free and cross-platform software that you can install and run on any laptop of your choice. Below you find the link to download and install R, Rstudio, and Gephi on your device (macOS, Windows, or Linux): 
Requirements, Exclusions and Recommendations

Not applicable to this module.

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: Essay between 2700-3300 words (excluding diagrams, graphs, images, or bibliography). A 3000-word essay is roughly equivalent to 6 pages single-spaced or 12 pages double-spaced Coursework (End of Trimester) n/a Graded Yes


Continuous Assessment: 200-word reaction papers addressing the weekly core readings of weeks 02-11 (between 270 and 330 words). This set of weekly reaction papers should be emailed to the Module Leader by Friday EOD. Throughout the Trimester n/a Graded Yes


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

How will my Feedback be Delivered?

Not yet recorded.

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Timetabling information is displayed only for guidance purposes, relates to the current Academic Year only and is subject to change.
Lecture Offering 1 Week(s) - 20, 21, 23, 24, 25, 26, 29, 31, 32, 33 Mon 10:00 - 11:50