Computational Social Science and Digital Issues

The details
Colchester Campus
Full Year
Undergraduate: Level 5
Thursday 03 October 2019
Friday 26 June 2020
24 May 2019


Requisites for this module



Key module for

BSC L310 Sociology with Data Science,
BSC L311 Sociology with Data Science (including Year Abroad),
BSC L312 Sociology with Data Science (including Placement Year),
BSC L313 Sociology with Data Science (including foundation Year)

Module description

With research methods rapidly changing in response to the large-scale generation of data within society, Social Science needs to ensure it is engaged with new digital methods to both benefit from them, and to shape them. In this module students will learn to combine their growing knowledge about society, social processes and research design, with powerful tools to both draw on and analyse the vast amounts and forms of new social data in a way that is critical, ethical and valuable.

This module provides a practical introduction to a range of methods that utilise intensive computational processing. Students will be taught in Python, a general-purpose accessible programming language popular in data science but also used widely in a vast range of sectors with growing demand. Students are not expected to have any prior programming experience, making this a valuable opportunity to learn new research techniques, as well as a skill that is in great demand.
In Autumn term students will learn how to generate their own datasets by drawing on social media platforms and build custom tools to scrape data from websites to build their own unique datasets. Students will also learn how to manage, clean and explore very large varied datasets in preparation for analysis whilst learning about data ethics, and the practical and social responsibilities of handling data. Throughout the module students will be given practical introductions to a range of analytical techniques within Social Network Analysis and Computational Content Analysis, learning how to find patterns in data through unsupervised machine learning, topic modelling, document clustering and sentiment analysis, and visualise networks of users, finding influencers and understanding patterns of social connection.

As part of the programme students will be given access to the University of Essex High Performance Cluster Computer for learning and independent projects, as well as free access to the online learning platform DataCamp for supplementary independent learning.
In Spring term students will be encouraged to evaluate the most recent developments in media technologies and to interrogate the various uses and practices that surround them from a wide-ranging sociological perspective. As such, the module introduces students to a broad range of social phenomena arising across the globe through the application and conceptualisation of digital technologies - from the sociology of the virtual body and cyborg sociology, to the rise of cybercrime and identity theft, from the utopian ideals of virtual democracy to the Orwellian nightmare of the surveillance society, from the free software movement to the hacker ethic and pirate politics.
*Note: The Spring component of SC207 is also available via SC224

Module aims

The course aims to provide students with…

A basic knowledge of the Python programming language

The ability to acquire data both through API’s and the web.

Knowledge and understanding of cleaning, managing and reporting on large datasets.

The ability to perform basic Social Network Analysis, and visualise networks using Gephi.

The ability to perform basic text analysis and topic modelling.

Knowledge and understanding of the legal and ethical issues surrounding computational social science practice.

Module learning outcomes

By the end of the course students should…

1. Have a fundamental proficiency in the Python programming language.
2. Be able to generate new datasets through the use of a social media API
3. Be able to generate new datasets through the creation of custom web scraping tools.
4. Be able to store, query and clean large datasets of varied data.
5. Understand the impact of different pre-processing techniques on later analysis outcomes.
6. Be able to visualise and measure social networks using Gephi
7. Be able to find themes and patterns in textual data using topic modelling and document clustering.
8. Be able to clearly communicate method and findings both through visualisations and written reports.
9. Understand the ethical and legal dimensions of computational social science.
10. Be able to situate computational techniques within broader principles of research design in the social sciences.
11. Be able to situate computational social science practice in a larger landscape of social issues related to computational processes.

Module information

100% Coursework:
Report 1 – 1,500 words worth 30%
Report 2 – 1,500 words worth 30%
Presentation – 5-10 minutes worth 10%
Essay – 2,500 words worth 30%

Learning and teaching methods

Teaching in the Autumn term will be delivered through ten up to 3-hour lab sessions. Each session will focus on practical, hands-on experience with a different tool/technique, interspersed with short lectures and demonstrations. The majority of the time students will work through guided interactive notebooks with teaching staff on hand to support learning, offer advice on project ideas and foster independent interests in the field. Students will also be provided with access to the online learning platform DataCamp, which provides interactive tutorials on a vast range of Data Science topics. Students will be expected to enhance their learning through the regular completion of DataCamp modules relevant to the course topics. Teaching in the Spring will be delivered through nine two hour seminars which will encompass lectures, workshops, group activities and discussion. Students will be expected to engage in independent learning activities including reading of set texts.


  • Mitchell, William J. (c2003) Me++: the cyborg self and the networked city, Cambridge, Mass: MIT Press.
  • Matthewman, Steve. (2011) Technology and social theory, Basingstoke: Palgrave Macmillan.
  • Gray, Chris Hables. (1995) The cyborg handbook, New York: Routledge.
  • Analyzing Social Media Data in Python | DataCamp,
  • Lessig, Lawrence. (2004) Free culture: how big media uses technology and the law to lock down culture and control creativity, New York: Penguin Press.
  • Gane, Nicholas; Beer, David. (2008) New media: the key concepts, Oxford: Berg.
  • Python Functions | DataCamp,
  • Python: Network Analysis | DataCamp,
  • Pandas Foundations | DataCamp,
  • Coleman, E. Gabriella; Golub, Alex. (2008) 'Hacker practice: Moral genres and the cultural articulation of liberalism', in Anthropological Theory. vol. 8 (3) , pp.255-277
  • Allen-Robertson, James. (2013) Digital culture industry: a history of digital distribution, Basingstoke: Palgrave Macmillan.
  • Dovey, Jon; Kennedy, Helen W. (2006) Game cultures: computer games as new media, Maidenhead: Open University Press.

The above list is indicative of the essential reading for the course. The library makes provision for all reading list items, with digital provision where possible, and these resources are shared between students. Further reading can be obtained from this module's reading list.

Assessment items, weightings and deadlines

Coursework / exam Description Deadline Weighting
Coursework   Report 1    30% 
Coursework   Report 2    30% 
Coursework   Essay    30% 
Practical   Presentation     10% 

Overall assessment

Coursework Exam
100% 0%


Coursework Exam
100% 0%
Module supervisor and teaching staff
Dr James Allen-Robertson, email:
Dr Michael Bailey, email:
Dr James Allen-Robertson (AU); Dr Michael Bailey (SP/SU)
Jane Harper, Student Administrator, Telephone: 01206 873052 E-mail:



External examiner

Dr Lorien Jasny
Available via Moodle
Of 58 hours, 22 (37.9%) hours available to students:
36 hours not recorded due to service coverage or fault;
0 hours not recorded due to opt-out by lecturer(s).


Further information

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