Social Networks, Text Analysis and Project - 2025 entry
| MODULE TITLE | Social Networks, Text Analysis and Project | CREDIT VALUE | 30 |
|---|---|---|---|
| MODULE CODE | COMM447Z | MODULE CONVENER | Dr Federico Botta (Coordinator) |
| DURATION: TERM | 1 | 2 | 3 |
|---|---|---|---|
| DURATION: WEEKS | 11 |
| Number of Students Taking Module (anticipated) |
|---|
DESCRIPTION - summary of the module content
The rise of the Web has created huge datasets relating to the interaction of users and online content. Much of this content is relational and is best understood using a network perspective (for example, hyperlinked web pages; users linking to content; users linking to users on social platforms). Much of this content consists of unstructured text (for example, webpages, blogs, social media posts) that requires computational methods for analysis at scale. In this module you will learn the core principles of social network analysis and computational text analysis, enabling you to gain insight from the rich data available on the Web.
Research Project: In this module, you will work on a research problem in an area relating to your programme of study, applying the tools and techniques that you have learned throughout the modules of the programme. This is an independent project, supervised by an expert from the relevant area, and culminates in writing a dissertation in the form of a research paper, describing your research and its results.
AIMS - intentions of the module
The aim of this module is to equip you with a range of knowledge and skills needed to make effective use of data from the Web. This module will cover various topics in social network analysis and text analysis, which together allow relational and unstructured text data to be analysed at scale. The module will be taught using the Python language and various open-source packages.
The module will be taught in weekly lectures and associated practical work, together with individual self-study and labs. Lectures will introduce the topics of social network analysis and text analysis, accompanied by practical exercises based on lecture material. Assessments will include assessed pitch-deck presentation of the mini-project and an individual mini-project involving the applications of social network and text analysis.
INTENDED LEARNING OUTCOMES (ILOs) (see assessment section below for how ILOs will be assessed)
On successful completion of this module you should be able to:
Module Specific Skills and Knowledge
1. Discuss the use of social network analysis for gaining insight from relational datasets
2. Demonstrate competence in core techniques in social network analysis and text analysis.
2. Demonstrate competence in core techniques in social network analysis and text analysis.
Discipline Specific Skills and Knowledge
3. Use computational methods to analyse complex datasets.
4. Use appropriate visualisation techniques to explore and communicate complex datasets.
4. Use appropriate visualisation techniques to explore and communicate complex datasets.
Personal and Key Transferable / Employment Skills and Knowledge
5. Communicate ideas, techniques and results fluently using written means appropriate for the intended audience.
6. Communicate data analysis procedures using notebooks and other digital media appropriate for a specialist audience
6. Communicate data analysis procedures using notebooks and other digital media appropriate for a specialist audience
SYLLABUS PLAN - summary of the structure and academic content of the module
Social network analysis topics will include:
- What is a network?
- Describing networks
- Visualising networks
- Network models: random networks, small-world networks, and scale-free networks
- Community detection algorithms, such as the Louvain algorithm
- Centrality measures, such as degree centrality and PageRank centrality
- Information spread
- Multiplex of Networks.
Text analysis topics will include:
- Working with text data
- Machine learning methods for text analysis
- TF-IDF
- Topic modelling
- Bag of words models
LEARNING AND TEACHING
LEARNING ACTIVITIES AND TEACHING METHODS (given in hours of study time)
| Scheduled Learning & Teaching Activities | 68 | Guided Independent Study | 232 | Placement / Study Abroad | 0 |
|---|
DETAILS OF LEARNING ACTIVITIES AND TEACHING METHODS
| Category | Hours of study time | Description |
| Scheduled Learning & Teaching activities | 68 | Asynchronous online learning activities, skill-based exercises and practical work |
| Guided independent study | 200 | Project work |
| Guided independent study | 32 | Background reading and self-study Including preparation for online content, reflection on taught material, wider reading and completion of assessments |
ASSESSMENT
FORMATIVE ASSESSMENT - for feedback and development purposes; does not count towards module grade
| Form of Assessment | Size of Assessment (e.g. duration/length) | ILOs Assessed | Feedback Method |
|---|---|---|---|
| Practical exercises, Weekly assigned work/exercises/forum discussion at the end of each sub-session and the end of week activities | 1 hour per week | All | Written feedback provided summarising performance and key areas for improvement |
SUMMATIVE ASSESSMENT (% of credit)
| Coursework | 100 | Written Exams | 0 | Practical Exams | 0 |
|---|
DETAILS OF SUMMATIVE ASSESSMENT
| Form of Assessment | % of Credit | Size of Assessment (e.g. duration/length) | ILOs Assessed | Feedback Method |
|---|---|---|---|---|
| Project task (practical work and report) | 70 | Code notebook and written report | All | Written |
| Pitch Deck Project Presentation | 30 | 90 hours | All | Written |
DETAILS OF RE-ASSESSMENT (where required by referral or deferral)
| Original Form of Assessment | Form of Re-assessment | ILOs Re-assessed | Time Scale for Re-assessment |
|---|---|---|---|
| Project task (practical work and report) | Code notebook and written report 70%) | All | Referral/deferral period |
| Pitch Deck Project Presentation | Pitch Deck Project Presentation (30%) | All |
Referral/deferral period
|
RE-ASSESSMENT NOTES
Reassessment will be by coursework in the failed or deferred element only. For referred candidates, the module mark will be capped at 50%. For deferred candidates, the module mark will be uncapped.
RESOURCES
INDICATIVE LEARNING RESOURCES - The following list is offered as an indication of the type & level of
information that you are expected to consult. Further guidance will be provided by the Module Convener
information that you are expected to consult. Further guidance will be provided by the Module Convener
Basic reading:
- Newman, M. E. J., Networks: An Introduction, Oxford University Press, 2010, 978-0199206650
- Ernesto, Estrada and Philip A. Knight, A first course in network theory, University of Oxford Press, 2015, 9780198726463
- Barbasi, A., and M. Posfai, Network Science, Cambridge University Press, 2016
- Caldarelli, Guido and Alessandro Chessa, Data Science and Complex Networks: Real Case Studies with Python, Oxford University Press, 2016
- Ignatow, G. and R. Mihalcea, Text Mining: A Guide for the Social Sciences, Sage, 2016
- Sarkar, D., Text Analytics with Python: A Practical Real-world Approach to Gaining Actionable Insights from your Data, Apress, 2016
Web based and Electronic Resources:
- ELE.
Other Resources:
Reading list for this module:
| CREDIT VALUE | 30 | ECTS VALUE | 15 |
|---|---|---|---|
| PRE-REQUISITE MODULES | None |
|---|---|
| CO-REQUISITE MODULES | None |
| NQF LEVEL (FHEQ) | 7 | AVAILABLE AS DISTANCE LEARNING | Yes |
|---|---|---|---|
| ORIGIN DATE | Tuesday 30th September 2025 | LAST REVISION DATE | Wednesday 8th October 2025 |
| KEY WORDS SEARCH | Social networks, social media, web, text analysis, text mining |
|---|
Please note that all modules are subject to change, please get in touch if you have any questions about this module.
