Study information

Social Networks and Text Analysis - 2025 entry

MODULE TITLESocial Networks and Text Analysis CREDIT VALUE15
MODULE CODECOM3029 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.
 
Pre-requisite modules: COM3028 or ECM1400.
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 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
 
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 Excercises

18 hours

All

Oral

 

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

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, 70%)

All

Summer reassessment period with a deadline in August

Pitch Deck Project Presentation

Pitch Deck Project Presentation (30%)

All

Summer reassessment period with a deadline in August

 

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
  • ELE

Reading list for this module:

Type Author Title Edition Publisher Year ISBN
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 Ernesto Estrada, Philip A. Knight A first course in network theory Oxford University Press 2015 9780198726463
Set Ignatow, G. & Mihalcea, R. Text Mining: A Guide for the Social Sciences. Sage 2016
Set Newman, M.E.J. Networks: An Introduction Oxford University Press 2010 978-0199206650
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 COM3028, ECM1400
CO-REQUISITE MODULES
NQF LEVEL (FHEQ) 6 AVAILABLE AS DISTANCE LEARNING No
ORIGIN DATE Tuesday 11th March 2025 LAST REVISION DATE Wednesday 9th April 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.