Social Systems

Domain Research

Domain Co-Leaders: Dr Timothy Graham and Dr Char-lee Moyle

The Social Systems domain encompasses data science research focused on the patterned networks of relationships involving individuals, groups and institutions. We define institutions broadly to encompass all collective human-mediated action, such as government, policies, strategies, law, companies, not-for-profit organisations, business or industry norms, social norms, cultural beliefs or the general patterns of consumer behaviour.

Some examples of research undertaken within our domain include data privacy and transparency, information disorder (e.g. mis- and dis-information), social and political movements, controversial societal issues (like anti-vaccination), social polarisation, social progress, community resilience, decent work, innovation and entrepreneurship, education, emerging industry clusters, evidence-based decision-making, strategy tracking, the adoption of sustainability, diversity and social good causes, computer-human interaction and the social-technical divide. Data science tools we draw on include causal testing, cluster analysis, multiple imputation, real-time trend tracking, bibliometrics and text data mining, sentiment analysis, network science, and micro-data analysis.

While our domain is broad, we do have core strengths in cybersecurity, social network analysis, bibliometrics, text mining and social media analytics. We have synergies and connections across QUT into areas of strategic investment such as QUT Entrepreneurship, the Centre for Future Enterprise, the Centre for Decent Work, the Behavioural Economics Group, and the Digital Media Research Centre. We have strong links into government and industry, working with organisations like DSDTI, LGAQ, Councils, Human Services, Foreign Affairs, Defence, Science and Technology, the Australia Institute, and the Centre for Responsible Technology. We host regular social good datathons and support internships to translate our work into tackling real world problems.

Associate Investigators

Data Science @ QUT Representatives

Students