Module Details

The information contained in this module specification was correct at the time of publication but may be subject to change, either during the session because of unforeseen circumstances, or following review of the module at the end of the session. Queries about the module should be directed to the member of staff with responsibility for the module.
Title Big Data and Society: Algorithms and Platforms B
Code COMM754
Coordinator Dr TE Nicholls
Communication and Media
Year CATS Level Semester CATS Value
Session 2021-22 Level 7 FHEQ Second Semester 15


This module aims to develop students’ critical understanding of the role of algorithms in online communication and in the political economy of platforms. It aims for students to learn about the algorithms that influence the development of online social systems, and also to critically examine the real-world consequences of algorithmic choices and the ways in which the design of platforms affect the world. The course emphasises a hands-on approach to studying algorithms in practice, so it aims to develop students’ programming skills to implement and explore their effects. A final aim is to develop a critical and theoretical approach to understanding the political and economic consequences of platforms.

Learning Outcomes

(LO1) Students will learn about important algorithms with implications for how we understand online communication.

(LO2) Students will explore the challenges of algorithmic decision-making, and some of its real-world implications.

(LO3) Students will interpret the political and economic role of online platform companies.

(LO4) Students will learn about the legal frameworks underpinning the Internet, and issues in the regulation of online speech.

(S1) Students will develop their ability to implement algorithms in software.

(S2) Students will be able to draw links between algorithms and wider effects, and interpret their effects.

(S3) Students will develop their skills in building and presenting an argument while selecting appropriate sources.

(S4) Students will further develop their academic writing skills.

(S5) Students will develop a critical and theoretical approach to understanding the political and economic consequences of platforms.



This module follows on from the previous core module, Big Data and Society 1: foundations, politics and policy. It goes into greater technical depth on algorithms (including by programming them in R) and their impacts, and also critically engages with key political and economic questions around platforms and their business models. We will continue to use Matthew Salganik’s Bit By Bit as one key text. This is available in print, but also as a freely-available online book.

On the algorithms side the course will address core questions such as how text is represented; search and recommender algorithms and associated network effects; algorithmic fairness, bias and discrimination; the transparency and explainability of algorithms; and the position of traditional media outlets in an algorithmically-mediated world. For the political economy of platforms section, the course will cover areas such as digital inequalities; privacy, identity, and data protection; advertising technol ogies, social media business models, surveillance, and the customer-as-product; platform capitalism, economics, and intermediation; speech, censorship, and moderation; and online law and regulation.

Teaching and Learning Strategies

Summary of Learning and Teaching Methods:
Teaching method: Workshops
Description: Weekly workshops, combining teaching and practical work
This activity may be online or on campus and could be subject to changes.
Schedule directed student hours: 24
Unscheduled directed student hours: 126
Attendance recorded: YES
Description of how self-directed learning hours may be used: Reading key and suggested literature, reviewing lecture slides, researching and writing the formative coursework, development of coding and analysis skills, researching and writing the summative coursework.

Teaching Schedule

  Lectures Seminars Tutorials Lab Practicals Fieldwork Placement Other TOTAL
Study Hours           24

Timetable (if known)           120 mins X 1 totaling 24
Private Study 126


EXAM Duration Timing
% of
Penalty for late
CONTINUOUS Duration Timing
% of
Penalty for late
Summative essay Resit opportunity - Yes Anonymous - Yes  words    100       
Formative essay Resit opportunity - No Anonymous - No  words         

Recommended Texts

Reading lists are managed at Click here to access the reading lists for this module.