- Creighton, M., Capistrano, D., Sorokowska, A., & Sorokowski, P. (2021). Physical Spacing and Social Interaction Before the Global Pandemic. Spatial Demography, 1-10.
- Fernandez de Arroyabe, J. C., Schumann, M., Sena, V., & Lucas, P. (2021). Understanding the network structure of agri-food FP7 projects: An approach to the effectiveness of innovation systems. Technological Forecasting and Social Change, 162, 120372.
- Sanford, M., Painter, J., Yasseri, T., & Lorimer, J. (2021). Controversy around climate change reports: a case study of Twitter responses to the 2019 IPCC report on land. Climatic Change, 167(3), 1-25.
- Carol, S., Kuipers, C., Koesling, P., & Kaspers, M. (2021). Ethnic and Religious Discrimination in the Wedding Venue Business: Evidence from Two Field Experiments in Germany and Austria. Social Problems.
- Dinh, R., Gildersleve, P., Blex, C., & Yasseri, T. (2021). Computational courtship understanding the evolution of online dating through large-scale data analysis. Journal of Computational Social Science, 1-26.
- Blex, C., & Yasseri, T. (2020). Positive algorithmic bias cannot stop fragmentation in homophilic networks. The Journal of Mathematical Sociology, 1-18.
- Ternovski, J., & Yasseri, T. (2020). Social complex contagion in music listenership: A natural experiment with 1.3 million participants. Social Networks, 61, 144-152.
- Feliciani, T., Moorthy, R., Lucas, P., & Shankar, K. (2020). Grade Language Heterogeneity in Simulation Models of Peer Review. Journal of Artificial Societies and Social Simulation, 23(3).
- Gusciute, E., Mühlau, P. & Layte, R. (2022). Discrimination in the Rental Housing Market: A Field Experiment in Ireland. Journal of Ethnic and Migration Studies, 48:3, 613-634.
- Gusciute, E., Mühlau, P. & Layte, R. (2022). One Hundred Thousand Welcomes? Economic Threat and Anti-immigration Sentiment in Ireland, Ethnic and Racial Studies, 45:5, 829-850.
Today the vast amount of digital data produced as a byproduct of our daily activities on digital platforms are harnessed and analysed using advanced statistical methods and machine learning to answer old and new social science questions. In addition, online experiments have become a key methodological tool in social science, which has resulted in notable opportunities to address core sociological questions. These innovative methods, advanced within quantitative and computational social science (QCSS), are actively expanding and re-shaping sociological enquiry. The QCSS research group studies innovation, environment-society interactions, crowd-sourcing, social discrimination, collective intelligence, conflict and collaboration, the sociology of machines, and online dating - among other topics. We use methods such as agent-based and mathematical modelling, large-scale quantitative surveys, network analysis, machine learning, and online, field, and natural experiments.
- Dr Ebru Esikli (Postdoctoral Research Fellow)
- Chenlong Wang (Phd student in Complex systems and Computational Social Science)
