1.To provide students with basic methods to answer social science research questions through data analysis
2.To introduce students with skills to design and analyse quantitative surveys
3.To teach students how to collect and organise datasets from digital media sources
4.To provide students with the empirical means to scaffold debates on online media at a large scale

(LO1) Students will show an understanding of broad foundational knowledge of research methods design, research philosophy and research ethics policy and practice.

(LO2) Students will demonstrate the ability to design suitable surveys to answer questions ranging from news consumption habits at scale to the use of digital media tools.

(LO3) Students will gain skills in probability analysis and statistical modelling for social sciences.

(LO4) Students will be able to collect data across digital media with a focus on social media and official news (BBC).

(LO5) Students will show familiarity with basic natural language processing techniques to analyse corpora at the content level (e.g. sentiment analysis, topic modelling).

(S1) Data wrangling

(S2) Hypothesis testing through statistical modelling

(S3) Working with API (application programming interface)

(S4) Automated text analysis (sentiment analysis, topic modelling, opinion mining)

The module content will be delivered in three blocks of lectures. Two weeks of the first of these (weeks 2-3) is delivered to all PGT students registered on Communication and Media programmes and provides broad foundational knowledge required for research methods design, philosophy and pathway towards preparation of a research project proposal.

BLOCK ONE (introduction)
Week 1 –Introduction to Computational Social Science Methods
Week 2 – Research methodologies and the research process: principles and frameworks

BLOCK TWO (social statistics)
Week 3 – Survey design
Week 4 – Fundamentals of statistics
Week 5 – Fundamentals of probability

BLOCK THREE (computational text analysis)
Week 6 - The ethics and practicalities of data collection from digital media
Week 7 - Reading week
Week 8 - Fundamentals of automated text analysis 1 (text as data, NLTK basics)
Week 9 - Fundamentals of automated text a nalysis 2
Week 10 - Fundamentals of automated text analysis 3
Week 11 - Fundamentals of automatic text generation
Week 12 - Guidance on how to write a dissertation proposal

Summary of Learning and Teaching Methods:
Teaching method: workshop
Description: three-hour workshop happening weekly for 12 weeks
Schedule directed student hours: 36
Unscheduled directed student hours: 114
Attendance recorded: YES
Notes:
Description of how self-directed learning hours may be used: reading module learning materials (key and suggested literature, lecture slides, additional reference material provided); assessment preparation