Study information

Social Networks and Text Analysis - 2025 entry

MODULE TITLESocial Networks and Text Analysis CREDIT VALUE15
MODULE CODEECMM447 MODULE CONVENERDr Federico Botta (Coordinator)
DURATION: TERM 1 2 3
DURATION: WEEKS 11 (October starters) 10 (January starters)
Number of Students Taking Module (anticipated) 80
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.

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. Discuss the use of text analysis for gaining insight from unstructured text datasets.
3. Demonstrate competence in core techniques in social network analysis and text analysis.
4. Use appropriate tools to analyse social network and text datasets, including the Python programming language and associated notebooks and packages.

Discipline Specific Skills and Knowledge

5. Use computational methods to analyse complex datasets.
6. Understand the role of network analysis and text analysis in the wider context of data science.
7. Use appropriate visualisation techniques to explore and communicate complex datasets.

Personal and Key Transferable / Employment Skills and Knowledge

8. Communicate ideas, techniques and results fluently using written means appropriate for the intended audience.
9. 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
• Community detection
• Centrality
• Information spread
• Multiplex of Networks.
 
 
Text analysis topics will include:
• Words, documents, corpora
• Bag-of-words, N-grams, feature extraction
• Supervised topic modelling
• Word2vec
• Introduction to Sentiment analysis.
LEARNING AND TEACHING
LEARNING ACTIVITIES AND TEACHING METHODS (given in hours of study time)
Scheduled Learning & Teaching Activities 34 Guided Independent Study 116 Placement / Study Abroad 0
DETAILS OF LEARNING ACTIVITIES AND TEACHING METHODS
Category Hours of study time Description
Scheduled Learning & Teaching 16 Lectures
Scheduled Learning & Teaching 18 Practical Work
Guided independent study 50 Project work
Guided independent study 66 Background reading and self-study

 

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 18 hours All Oral

 

SUMMATIVE ASSESSMENT (% of credit)
Coursework 100 Written Exams 0 Practical Exams
DETAILS OF SUMMATIVE ASSESSMENT
Form of Assessment % of Credit Size of Assessment (e.g. duration/length) ILOs Assessed Feedback Method
Mini-project (practical work and report) 70% Code notebook and 4 pages-word report All Written
Pitch Deck Project Presentation 30% 2 Weeks' workload 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
Mini-project (practical work and report) Mini project (practical work and report) (code notebook and 4-page word report, 70%) All Referral/deferral period
Pitch Deck Project Presentation Pitch Deck Project Presentation (2 weeks’ workload, 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
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 

Reading list for this module:

Type Author Title Edition Publisher Year ISBN
Set Newman, M.E.J. Networks: An Introduction Oxford University Press 2010 978-0199206650
Set Ernesto Estrada, Philip A. Knight A first course in network theory Oxford University Press 2015 9780198726463
Set Barabasi, A. & Posfai, M. Network Science. Cambridge University Press. 2016
Set Caldarelli, G. & Chessa, A. Data Science and Complex Networks: Real Case Studies with Python. Oxford University Press 2016
Set Ignatow, G. & Mihalcea, R. Text Mining: A Guide for the Social Sciences. Sage 2016
Set Sarkar, D. Text Analytics with Python: A Practical Real-world Approach to Gaining Actionable Insights from your Data. Apress 2016
CREDIT VALUE 15 ECTS VALUE 7.5
PRE-REQUISITE MODULES COMM109
CO-REQUISITE MODULES
NQF LEVEL (FHEQ) 7 AVAILABLE AS DISTANCE LEARNING No
ORIGIN DATE Thursday 14th March 2024 LAST REVISION DATE Tuesday 6th May 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.