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BAEcon Economics and Social Statistics / Course details
Year of entry: 2021
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Course unit details:
Understanding Social Media
|Unit level||Level 1|
|Teaching period(s)||Semester 2|
|Offered by||Social Statistics|
|Available as a free choice unit?||Yes|
What can social media data tell us about society and human behaviour? How can such data be used for social research and for tackling intractable social issues?
This module will provide an introduction to the theory and practice of using social media data for research and enable the development of transferable research and data skills. Such skills are in demand in the research and consultancy profession across the public and private sectors. After reviewing the different data types including Facebook and Twitter we consider how to access and analyse such data. This, in part, will include developing the student’s critical data skills, hands-on training and practice analyses on real social media data such as coding Tweets and blogs. This will involve the use of on-line software to gather social media data. The module will involve the development of research design skills including: hypothesis testing, data analysis and interpretation and writing skills. The emphasis on the use of real data to answer questions is designed to engage students and for them to consider using such approaches as part of their own dissertation research.
1. Twitter, Facebook, Instagram, You Tube - An Introduction to social media data and types.
2. How to analyse social media. Basic statistical skills for data analysis.
3. Practical session (1) – Software taster training and tasks.
4. Big Bad Data? What can you claim? Sampling and inference for social media data.
5. Analysing social media.
6. Measurement debates. Data quality issues and social media data eg: coverage, performance, fake accounts and ethics.
7. Practical session (2) Designing social research using social media.
8. Practical session (3) Data collection.
9. Practical session (4) Data analysis.
10. More data for social research? Linking social media data with other data sources for understanding society. Report writing skills, course overview and assignment
Tutorials. – Weekly focused practical skill sessions linked to the lectures.
The tutorials will form part of the formative assessment for the course where students present ideas and draft outlines for discussion and feedback. In addition, students will be given the opportunity to do a practice essay and will get feedback on their writing. Feedback will be given during tutors’ office hours in the last week of the semester.
The unit aims to:
(i). To develop the students understanding of social research methods using social media data such as Facebook, Instagram, Twitter and Blogs.
(ii). To inform students about research design and ethical issues concerning the use of social media data in research.
(iii). To introduce students to the analytical skills used in collecting and analysing social media data.
(iv). To provide students with a basic training in the use of software for the handling and the analysis of social media data.
(v). To develop students understanding and critical skills in such areas as sampling, sample bias and statistical inference in social research.
(vi). To enable students to develop and write a dissertation research proposal based around using such data should they choose to.
Knowledge and Understanding: A critical understanding of the evidence and debates regarding the use of social media data for understanding society.
Intellectual skills: An understanding of good practices in research design, evaluating evidence and data and assessing robustness. Develop critical skills in evaluating data and methods through lectures, lab sessions, group work and independent reading.
Practical skills: An understanding of social statistics and practical experience of data analysis including using software for social research. Develop skills in evaluating evidence and scientific claims.
Transferable skills and personal qualities: Critical data analysis and evaluation skills. Social statistics and data analysis skills are in high demand in the labour market. The group work will also aid the student in developing their communication and team working skills.
Teaching and learning methods
The module will involve: lectures, group work, lab classes as well as data gathering and analysis tasks.
Extensive use will be made of relevant on-line resources including: data archives, analysis and data visualisation tools and literature resources as well as video and radio recordings. Moreover, the data itself will be accessed on-line.
Blackboard resources will be used to enable students to access software for the collection and analysis of social media data.
The lecture component will provide a theoretical and methodological framework for learning about how to use social media data for research. Practical sessions will give students hands on experience in all aspects of data analysis and interpretation and using appropriate software for data manipulation. Such skills are highly transferable and in demand by employers.
The emphasis on the use of real data to answer questions is designed to engage students and for them to consider using such approaches as part of their own dissertation research.
Please note the information in scheduled activity hours are for guidance only and may change.
All Social Statistics courses include both formative feedback – which lets you know how you’re getting on and what you could do to improve – and summative feedback – which gives you a mark for your assessed work.
Bryman, A. (2016) Social Research Methods. Oxford University Press.
Burgess, J., Marwick, A., and Poell, T. (2019) The SAGE Handbook of Social Media. London: Sage
Cantijoch Cunill, M., Gibson, R. and Steven Ward, S. (2015) Analysing Social Media Data and Web Networks. London: Palgrave Macmillan.
Gruzd, A., and Mai, P. (2020). Going viral: How a single tweet spawned a COVID-19 conspiracy theory on Twitter. Big Data & Society. https://doi.org/10.1177/2053951720938405
Halfpenny, P. and Proctor, R. (eds) Innovations in Digital Social Research Methods. London: Sage
Hewson, C., Vogel, C. and Laurent, D. (2015) Internet Research Methods. London: Sage.
Howard, P. N. (2020) Lie Machines: How to Save Democracy from Troll Armies, Deceitful Robots, Junk News Operations, and Political Operatives. Yale University Press.
Kozinets, R. (2020) Netnography. London: Sage.
Making Sense of Statistics (2010) Sense About Science and Straight Statistics. http://www.senseaboutscience.org/data/files/resources/1/MSofStatistics.pdf
Margetts, H. John. P. Hale, S. and Yasseri, T. (2016) Political Turbulence: How Social Media Shape Collective Action. Princeton University Press. http://press.princeton.edu/titles/10582.html
Mellon, J. and Prosser, C. (2017) Twitter and Facebook are not representative of the general population: Political attitudes and demographics of British social media users. Research and Politics July-September, 1-9.
Ó Dochartaig, N. (2012) Internet Research Skills. London: Sage.
Sloan, L and Quan-Haase, A. (2018) The SAGE Handbook of Social Media Research Methods Hardcover. London: Sage.
Sloan, L., Morgan, J., Housley, W., Williams, Edwards, M., Burnap, A., Omer, R. (2013) Knowing the Tweeters: Deriving Sociologically Relevant Demographics from Twitter’. Sociological Research Online 18 (3) 7 www.socresonline.org.uk/18/3/7.html
United Nations (2012) Big Data for Development: Opportunities and Challenges: A Global Pulse White Paper. http://www.unglobalpulse.org/BigDataforDevWhitePaper
Zwitter, A. (2014) Big Data Ethics. Big Data & Society. 1-6
Online - Resources
Fake News Inquiry
The Joy of Stats by Hans Rosling
|Scheduled activity hours|
|Assessment written exam||2|
|Practical classes & workshops||10|
|Independent study hours|
|Kingsley Purdam||Unit coordinator|
|Ji Hye Kim||Unit coordinator|