Term 1 - Online
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Social networks and social platforms are a widely used technology for connecting individuals and connecting organisations. They can provide key insights into human and organizational behaviours and needs. This subject will introduce students to methods for analyzing data generated by social networks and social platforms.
The following topics will be covered: network structure and semantics, including friend‐follower relationships; social network analysis fundamentals including connectedness, centrality and influence; community detection; social network visualisation methods; combining text and social network analysis; user modelling, including prediction and recommendation strategies; gaining insights into groups of users via clustering/segmentation; trend monitoring in social networks; prediction and anomaly detection in networks; automated social interaction: conversational chatbots and their inferential capabilities and interfaces; case studies in public health surveillance, education and psychology.
Intended learning outcomes
On completion of this subject, students should be able to:
- Evaluate and apply key techniques used in social analytics and deploy them in combination for different scenarios
- Critique component technologies in commonly deployed systems that analyse social networks and be able to communicate issues relevant to the effective implementation and operation of such systems
- Explain and justify to others the use of social network analysis algorithms for real world use by individuals or organisations
Students will be provided with the opportunity to practice and reinforce:
- High level written communication skills
- Advanced information and interpretation skills
- Advanced analytic, integration and problem‐solving skills
- Demonstrate competence in critical and theoretical thinking through report writing and online discussions
Last updated: 5 December 2019