Computer Science

ECMM439 - Social Networks and Text Analysis (2019)

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MODULE TITLESocial Networks and Text Analysis CREDIT VALUE15
MODULE CODEECMM439 MODULE CONVENERDr Riccardo Di Clemente (Coordinator)
DURATION: TERM 1 2 3
DURATION: WEEKS
Number of Students Taking Module (anticipated) 20
DESCRIPTION - summary of the module content

***DATA SCIENCE AND DATA SCIENCE WITH BUSINESS STUDENTS ONLY***

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: ECMM430 Fundamentals of Data Science.
Co-requisites: None.

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 ECMM430 Fundamentals of Data Science.

The module will be taught in a one-week intensive block of lectures and associated practical work, together with individual self-study and coursework. 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.

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.

Text analysis topics will include:

• Words, documents, corpora.
• Bag-of-words, N-grams, feature extraction.
• Supervised/unsupervised topic modelling.
• Sentiment analysis.

LEARNING AND TEACHING
LEARNING ACTIVITIES AND TEACHING METHODS (given in hours of study time)
Scheduled Learning & Teaching Activities 34.00 Guided Independent Study 116.00 Placement / Study Abroad 0.00
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 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 & 2000 word report All Written
Assessed practical exercises 30 1 hour 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) All Within 8 Weeks
Assessed practical exercises Assessed practical exercises All Within 8 Weeks
       

 

RE-ASSESSMENT NOTES

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%.

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:

 

ELE: http://vle.exeter.ac.uk/

 

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 Easley, D., & Kleinberg, J. Networks, crowds, and markets: Reasoning about a highly connected world. Cambridge University Press. 2010 [Library]
Set Grus, J. Data Science From Scratch: First Principles With Python O'Reilly 2015 [Library]
Set McKinney, W. Python for Data Analysis: Data Wrangling with Pandas, Numpy and iPython 1st O'Reilly Media 2012 978-1449319793 [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]
CREDIT VALUE 15 ECTS VALUE 7.5
PRE-REQUISITE MODULES ECMM430
CO-REQUISITE MODULES
NQF LEVEL (FHEQ) 7 AVAILABLE AS DISTANCE LEARNING No
ORIGIN DATE Tuesday 10 July 2018 LAST REVISION DATE Tuesday 18 December 2018
KEY WORDS SEARCH Social networks, social media, web, text analysis, text mining