SC207-5-FY-CO:
Computational Social Science
2020/21
Sociology and Criminology
Colchester Campus
Full Year
Undergraduate: Level 5
Current
Thursday 08 October 2020
Friday 02 July 2021
30
25 June 2020
Requisites for this module
(none)
(none)
(none)
(none)
(none)
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)
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 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.
Using Python, 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.
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
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 store, query and clean large datasets of varied data.
4. Understand the impact of different pre-processing techniques on later analysis outcomes.
5. Be able to visualise and measure social networks using Gephi
6. Be able to find themes and patterns in textual data using topic modelling and document clustering.
7. Be able to clearly communicate method and findings both through visualisations and written reports.
8. Understand the ethical and legal dimensions of computational social science.
9. Be able to situate computational techniques within broader principles of research design in the social sciences.
Please note that assessment information is currently showing for 2019-20 and will be updated in September.
Outline Syllabus
Autumn Fundamentals
Week 2 Session 1 - What is Computational Social Science? Let's Get Started
Week 3 Session 2 - Python Fundamentals: Loops, Lists and Strings, oh my!
Week 4 Session 3 - Python Fundamentals: Functions, scopes and objects.
Week 5 Session 4 - Structuring and managing data with Pandas
Week 6 Session 5 - Exploring and visualising data with Pandas
APIs & Social Network Analysis
Week 7 Session 6 - The practice and problems of gathering Twitter data.
Week 8 Session 7 - Exploring and summarising Twitter Data
Week 9 Session 8 - Restructuring your data into a Network with Networkx
Week 10 Session 9 - Social Network Analysis with Gephi
Catch-up
Week 11 Session 10 - Catch up and code surgery
Spring Web scraping
Week 16 Session 11 - Understanding HTML and website structures
Week 17 Session 12 - Restructuring a webpage into a dataset
Week 18 Session 13 - Automated and robust, polite and ethical webscraping
Text Mining
Week 19 Session 14 - Introduction to text as data and entities
Week 20 Session 15 - Analysing and summarising collections of text
Week 21 No Session - Reading Week – Independent Project Time
Week 22 Session 16 - From terms to values: preparing text for AI analysis and discovering significant terms.
Week 23 Session 17 - Finding themes in text with topic models
Week 24 Session 18 - Testing and refining your topic models for accurate results.
Catch-up
Week 25 Session 19 - Catch-up and code surgery
Summer Presentations
Weeks 31/32 Students will be assigned to one of the scheduled presentation 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. Students typically complete a module within 7-10 days.
- Analyzing Social Media Data in Python | DataCamp, https://www.datacamp.com/courses/analyzing-social-media-data-in-python
- Pandas Foundations | DataCamp, https://www.datacamp.com/courses/pandas-foundations
- Researchers just released profile data on 70,000 OkCupid users without permission - Vox, https://www.vox.com/2016/5/12/11666116/70000-okcupid-users-data-release
- Feature engineering for NLP | DataCamp, https://learn.datacamp.com/courses/feature-engineering-for-nlp-in-python
- Web Scraping with Python | DataCamp, https://www.datacamp.com/courses/web-scraping-with-python
- Python: Network Analysis | DataCamp, https://www.datacamp.com/courses/network-analysis-in-python-part-1
- Natural Language Processing Fundamentals in Python | DataCamp, https://www.datacamp.com/courses/natural-language-processing-fundamentals-in-python
- Python Functions | DataCamp, https://www.datacamp.com/courses/python-data-science-toolbox-part-1
- Veltri, Giuseppe A. (2019-10-25) Digital Social Research: John Wiley and Sons Ltd.
- GEPHI – Introduction to Network Analysis and Visualization, http://www.martingrandjean.ch/gephi-introduction/
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 |
Coursework weighting |
| Coursework |
Report 1 |
29/01/2021 |
30% |
| Coursework |
Research Proposal |
26/03/2021 |
20% |
| Coursework |
Video Presentation |
14/05/2021 |
20% |
| Coursework |
Report 2 |
21/05/2021 |
30% |
Additional coursework information
Report 1 - Independent Social Media Data project. Students must submit both a report and code.
Report 2 – Independent text scraping project. Students must submit both a report and code.
Presentation – 5-10 minutes.
Essay – 2,500 words
Exam format definitions
- Remote, open book: Your exam will take place remotely via an online learning platform. You may refer to any physical or electronic materials during the exam.
- In-person, open book: Your exam will take place on campus under invigilation. You may refer to any physical materials such as paper study notes or a textbook during the exam. Electronic devices may not be used in the exam.
- In-person, open book (restricted): The exam will take place on campus under invigilation. You may refer only to specific physical materials such as a named textbook during the exam. Permitted materials will be specified by your department. Electronic devices may not be used in the exam.
- In-person, closed book: The exam will take place on campus under invigilation. You may not refer to any physical materials or electronic devices during the exam. There may be times when a paper dictionary,
for example, may be permitted in an otherwise closed book exam. Any exceptions will be specified by your department.
Your department will provide further guidance before your exams.
Overall assessment
Reassessment
Module supervisor and teaching staff
Dr James Allen-Robertson, email: jallenh@essex.ac.uk.
Dr Valentin Danchev, email: valentin.danchev@essex.ac.uk.
Dr James Allen-Robertson (AU); Dr Michael Bailey (SP/SU)
Jane Harper, Student Administrator, Telephone: 01206 873052 E-mail: socugrad@essex.ac.uk
Yes
Yes
No
Available via Moodle
Of 2163 hours, 0 (0%) hours available to students:
2163 hours not recorded due to service coverage or fault;
0 hours not recorded due to opt-out by lecturer(s).
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