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 Social Media Research Methods
Code COMM755
Coordinator Dr TE Nicholls
Communication and Media
T.E.Nicholls@liverpool.ac.uk
Year CATS Level Semester CATS Value
Session 2021-22 Level 7 FHEQ First Semester 15

Aims

This module aims to provide students with an overview of research methods as applied to social media research. It aims to teach necessary foundational knowledge for research design, as well as specific approaches. By developing students’ abilities to understand and interpret published research as well as to conduct their own projects, it aims to support both the taught modules and the dissertation module for the MA in Media, Data and Society. The data analysis techniques and R skills taught in the module also aim to support students in future data-driven work outside the academy.


Learning Outcomes

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

(LO2) Students will learn basic social statistics and their application to survey research

(LO3) Students will learn the foundations of computational social media research methods including different approaches to using text as data.

(LO4) Students will learn about using social media as a source of data for applied research projects

(S1) Students will gain skills in understanding and interpreting published survey research into social media use.

(S2) Students will develop a foundational level of programming skills using R

(S3) Students will gain skills in obtaining social media research data from open platforms, and in analysing and interpreting it.

(S4) Students will gain skills in conducting and interpreting data visualisation of social media data.

(S5) Students will develop skills in using data to solve problems.


Syllabus

 

The module content will be delivered in three blocks of teaching. The first of these (weeks 1-4) 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, which is the common assessment across all Communication and Media PGT programmes. Block 2 is shared with “Introduction to Computational Social Science Methods” from the MSc in Data Science and Communication.

BLOCK ONE – Common foundation
Week 1 – Introduction to module: 'what is research?'
Week 2 – Research methodologies: philosophical principles and frameworks
Week 3 – Research in practice: understanding primary and secondary research and analysis
Week 4 – Research ethics – issues, policy, best practice

BLOCK TWO – Survey research methods
Week 5  013; An introduction to social statistics
Week 6 – The foundations of survey research
Week 7 – Independent study week
Week 8 – Survey analysis

BLOCK THREE – Social media content analysis
Week 9 – Social media as a data source
Week 10 – An introduction to text analysis
Week 11 – An introduction to data visualisation


Teaching and Learning Strategies

Teaching method: Workshop

Description: Weekly lecture combined with hands-on application
This activity may be online or on campus and could be subject to changes.

Schedule directed student hours: 30

Unscheduled directed student hours: 120

Attendance recorded: YES

Notes: The module uses a mixed approach to learning and teaching, combining lecture-style delivery of core knowledge with the more active and experiential approach of practical work in a workshop environment. This approach is driven by the practical aims of the course, to support students’ own dissertation research, and also by the differing starting position of students coming from varying undergraduate disciplines. As the course is relatively small, the hands-on workshop format should allow tailored support to be given and students to develop as quickly as each is able to.
Description of how self-directed learning hours may be used: Reading key and suggested literature, review ing lecture slides, practicing and developing the statistics and programming skills developed, and preparing the summative coursework project.


Teaching Schedule

  Lectures Seminars Tutorials Lab Practicals Fieldwork Placement Other TOTAL
Study Hours           30

120

150
Timetable (if known)           180 mins X 1 totaling 30
 
 
Private Study 0
TOTAL HOURS 150

Assessment

EXAM Duration Timing
(Semester)
% of
final
mark
Resit/resubmission
opportunity
Penalty for late
submission
Notes
Interpretation of survey findings Resit opportunity – Y Anonymous - Y In-class test  60 minutes    40       
CONTINUOUS Duration Timing
(Semester)
% of
final
mark
Resit/resubmission
opportunity
Penalty for late
submission
Notes
Social media content analysis Resit opportunity - Y Anonymous - Y  words    60       

Recommended Texts

Reading lists are managed at readinglists.liverpool.ac.uk. Click here to access the reading lists for this module.