De, S., Jangra, S., Agarwal, V., Johnson, J., & Sastry, N. 2023. Biases and Ethical Considerations for Machine Learning Pipelines in the Computational Social Sciences. In Ethics in Artificial Intelligence: Bias, Fairness and Beyond (pp. 99-113). Singapore: Springer Nature Singapore
Sharifian-Attar, V., De, S., Jabbari, S., Li, J., Moss, H. 2022. Analysing Longitudinal Social Science Questionnaires: Topic modelling with BERT-based Embeddings IEEE International Conference on Big Data Special session MLBD 2022
Johnson, J., Lukose, E., De, S. 2022. Privacy Pitfalls of Online Service Terms and Conditions: a Hybrid Approach for Classification and Summarization. NLLP 2022 Workshop at EMNLP
De, S., Moss, H., Johnson, J., Li, J., Pereira, H. and Jabbari, S. 2022. Engineering a machine learning pipeline for automating metadata extraction from longitudinal survey questionnaires. IASSIST Quarterly. 46, 1 (Mar. 2022)
De, S., Johnson, J., Li, J., Moss, H. and Jabbari, S. 2022. Understanding the multiple dimensions of prediction of concepts in social and biomedical science questionnaires. DiRAC Federation Project, D-FED 3.1.7
Johnson, Jon., De, Suparna. 2023. Initial findings from the automation of extraction of metadata from questionnaires and its classification. ESRA 2023, Milan, Italy
De, Suparna., Moss, Harry., Jabbari, Sanaz., Pereira, Haeron., Johnson, Jon., Li, Jenny. 2021. Engineering a Machine Learning Pipeline for Automating Metadata Extraction from Longitudinal Survey Questionnaires European DDI Users Conference 2021, Paris, France
Metadata Uplift and Machine Learning - European Perspectives. November 2023 European DDI Users Conference, Ljubljana, Slovenia