Digital Society Research Cluster

Digital technologies and data science are influencing every aspect of our society. From the news we read and listen to on different platforms, to the recommended products on electronic commerce websites and our interactions with local and national government agencies. The digital society cluster will bring together researchers from the Data Science Research Centre to work and collaborate with researchers from the business school, law and social sciences, psychology, the environmental sciences, and the built environment to address some of the challenges within modern society. These challenges are complex, multidimensional and require collaboration from different disciplines.

Our aims

Working with academic colleagues across the University and our partners from the industry and voluntary sector, the cluster aims to shape the present and future societies using available technologies to combat crime, poverty, depravation and climate change, while encouraging the delivery of better education, transport and other services to communities. The cluster has areas of academic expertise including:

Natural Language Processing

We have expertise in Natural Language Processing and Understanding. Applications include text generation, machine translation and information retrieval. Our project with Bloc Digital involves the automatic generation of product descriptions using GPT models.

AI for Social Justice, Security and Culture

The analysis of socioeconomic data is used to provide managers and users with the ability to make data-driven decision-making. This includes planning for services such as health, education and transport or understanding crime patterns. The centre is participating in an international project examining how social network analysis could disrupt some of the world’s most dangerous criminal gangs.

Another research area for the Centre is looking at removing bias in AI. AI models can be biased towards specific socio-cultural groups if the datasets used are not representative of the population’s studies.  

Business Applications of Data Science

Data science is extensively used in business, marketing and finance, and this is often referred to as business intelligence. Machine learning for example, is used to predict the bankruptcy of companies based on some financial data, credit risks and in general to allow organisations to make more data-driven decisions. Another area of expertise of the Centre is the development of recommendation systems and sentiment analysis to understand customers’ behaviour leading to better and more targeted marketing.  

Research Cluster Team

Join us

If you are interested in joining this research centre, want to find out more or are interested in applying for a PhD in this area, please contact Dr Ovidiu Bagdasar.


  • Bello Musa Yakubu, Majid Iqbal Khan, Khan, A., Adeel Anjum, Madiha Syed and Semeen Rehman 2023. A Privacy-Enabled, Blockchain-Based Smart Marketplace. Applied Sciences. 13 (5), pp. 1-16.   
  • Yakubu, M, Y., Khan, M. I., Khan, A., Jabeen, F. and Jeon, G. 2023. Blockchain-based DDoS attack mitigation protocol for device-to-device interaction in smart homes. Digital Communications and Networks. pp. 1-15.  
  • A Shakoor, S Ali, M Irfan, J Muhammad, A Khan 2022. Human Rights Violation Reports in English and Urdu Press during Democracies and Dictatorial Regimes in Pakistan from 2002 to 2013.  Central European Management Journal 30 (4), 502-515 
  • Swee, C.P., Labadin, J. and Meziane, F. 2022. Credit Risk Prediction for Peer-To-Peer Lending Platforms: An Explainable Machine Learning Approach. Journal of Computing and Social Informatics. 1 (2), pp. 1-16. 
  • Ficara, Annamaria, Curreri, Francesco, Cavallaro, Lucia, De Meo, Pasquale, Fiumara, Giacomo, Bagdasar, Ovidiu and Liotta, Antonio 2021. Social network analysis: the use of graph distances to compare artificial and criminal networks. Journal of Smart Environments and Green Computing. 
  • Ficara, Annamaria, Cavallaro, Lucia, Curreri, Francesco, Fiumara, Giacomo, De Meo, Pasquale, Bagdasar, Ovidiu, Song, Wei and Liotta, Antonio 2021. Criminal networks analysis in missing data scenarios through graph distances. PLos ONE. 16 (8), p. e0255067. 
  • A Abdi, G Sedrakyan, B Veldkamp, J van Hillegersberg, SM van den Berg (2023). Students feedback analysis model using deep learning-based method and linguistic knowledge for intelligent educational systems. Soft Computing, 1-22