Computer Science

COMM419DA - Social Networks and Text Analysis (Professional) (2023)

Back | Download as PDF
MODULE TITLESocial Networks and Text Analysis (Professional) CREDIT VALUE15
MODULE CODECOMM419DA MODULE CONVENERDr Riccardo Di Clemente (Coordinator)
Number of Students Taking Module (anticipated) 90
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 (e.g. hyperlinked web pages; users linking to content; users linking to users on social platforms). Much of this content consists of unstructured text (e.g. 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-requisites: COMM415DA Fundamentals of Data Science (Professional)
Co-requisites: None
This module is a part of the dual-qualification MScData Science (Professional / Level 7 Research Scientist Apprenticeship programme. It cannot be taken as an elective by students on other programmes.  
The apprenticeship standard and other documentation relating to the Level 7 Research Scientist Apprenticeship can be found here:


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, and will assume no knowledge beyond the mathematics and programming covered in pre-requisite COMM415 Fundamentals of Data Science.
 Lectures will introduce the topics of social network analysis and text analysis, accompanied by practical exercises based on lecture material. Assessments will include assessed practical exercises and an individual mini-project involving the application of social network and text analysis.
In addition to its academic aims as part of the programme, this module has specific aims as part of the Level 7 Research Scientist Apprenticeship. The full list of Knowledge, Skills and Behaviours that must be demonstrated to complete the Apprenticeship can be found here:
This module will deliver content that may be used to evidence the Knowledge, Skills and Behaviours set out below. Primarily: K1, K4 and K5. Secondarily: S1, S2 and S6.
Knowledge (K), Skill (S) or Behaviour (B)
K1: Subject specific knowledge: A deep and systemic understanding of a named / recognised scientific subject as found in an industrial setting, such as biology, chemistry or physics, found in the nuclear, food manufacture, pharmacology or energy production sectors, at a level that allows strategic and scientific decision making, while taking account of inter relationships with other relevant business areas / disciplines.
K4: Research methodologies: Methodologies appropriate to the sector and how to formulate and apply a hypothesis. Appropriate application of scientific process. The unpredictability of research projects and the need to adapt and adjust daily planning needs to accommodate new developments. 
K5: Data analysis and evaluation: Statistical analysis techniques, numerical modelling techniques and how they are applied in context. How to interpret and categorise data to make informed and objective decisions against the goals and targets of the project. How to evaluate and interpret the data and associated analysis against company objectives.
S1: Scientific Knowledge: Apply a range of advanced, new and emerging practical and experimental skills appropriate to the role (e.g. chemical synthesis, bio analysis, computational modeling). 
S2: Data Collection and Reporting: Capture and evaluate data critically drawing a logical conclusion, e.g. Case Report Forms, Data Management Plans, Data Review Plans, edit checks and User Acceptance Testing Plans.
S6: Critical Thinking: Conceptualise, evaluate and analyse information to solve problems.
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.
  • 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.
Scheduled Learning & Teaching Activities 40.00 Guided Independent Study 110.00 Placement / Study Abroad 0.00
Category Hours of study time Description
Scheduled Learning & Teaching 40 Lectures, workshops, practical work
Guided independent study 110 Reading, preparation, coursework work


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


Coursework 100 Written Exams 0 Practical Exams 0
Form of Assessment % of Credit Size of Assessment (e.g. duration/length) ILOs Assessed Feedback Method
Mini-project (practical work and report) 100 3-page report 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) (100%) All Within 8 Weeks



Deferral – if you miss an assessment for certificated reasons judged acceptable by the Mitigation Committee, you will normally be either deferred in the assessment or an extension may be granted. The mark given for a reassessment taken as a result of deferral will not be capped and will be treated as it would be if it were your first attempt at the assessment.

Referral – if you have failed the module overall (i.e. a final overall module mark of less than 50%) you will be required to re-take some or all parts of the assessment, as decided by the Module Convenor. The final mark given for a module where re-assessment was taken as a result of referral will be capped at 50%.

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:




Web based and Electronic Resources:


Other Resources:


Reading list for this module:

Type Author Title Edition Publisher Year ISBN Search
Set Newman, M.E.J. Networks: An Introduction Oxford University Press 2010 978-0199206650 [Library]
Set Caldarelli, G. & Chessa, A. Data Science and Complex Networks: Real Case Studies with Python. Oxford University Press 2016 [Library]
Set Barabasi, A. & Posfai, M. Network Science. Cambridge University Press. 2016 [Library]
Set Ignatow, G. & Mihalcea, R. Text Mining: A Guide for the Social Sciences. Sage 2016 [Library]
Set Sarkar, D. Text Analytics with Python: A Practical Real-world Approach to Gaining Actionable Insights from your Data. Apress 2016 [Library]
Set Ernesto Estrada, Philip A. Knight A first course in network theory Oxford University Press 2015 9780198726463 [Library]
ORIGIN DATE Tuesday 10 July 2018 LAST REVISION DATE Tuesday 24 January 2023
KEY WORDS SEARCH Social networks, social media, web, text analysis, text mining