FAM5038S
DIGITAL METHODS FOR SOCIAL MEDIA RESEARCH
24 NQF credits at NQF level 9
Convener: A/Prof Marion Walton
Course entry requirements: Acceptance for a master’s programme.
This course introduces students to practical and analytical techniques for Internet-related research in African contexts. Practical training is informed by decolonial and intersectional feminist perspectives on data, infrastructure and artificial intelligence, as well as critical and ethical reflection on contemporary social issues. Students are also introduced to current industry practices and tools for social media analytics and given a grounding in relevant legal and ethical considerations for research with social media data. Computational and analytical skills are developed via a set project and scaffolded exercises which will equip students to approach certain “large qualitative” research questions and help them hone research questions focused on user practices, social media content, companies, applications or the interaction between platforms and publics.
Exercises will guide students through (i) data collection using publicly accessible data and tools, (ii) data cleaning and basic descriptive analysis and visualisation, (iii) data storage and encryption, and (iv) basic concepts for network analysis or multimodal content analysis. The course also provides an overview of mixed methods and literature from the global South which will help students explore digital datasets in the context of local practices, political economy and infrastructural realities. A range of topics and qualitative methods are reviewed with the aim of contextualising online media and user practices, particularly in African contexts. These may include contextual enquiry, virtual ethnography, in-depth interviewing, app and device walkthroughs, heuristic evaluation as critical analysis, screenshot diaries, or other suitable user experience (UX) research methods (such as A/B testing). Students will integrate the knowledge gained through exercises and their review of relevant studies and methods by preparing an original research proposal and dataset suitable for use in an MA dissertation