Senior Lecturer and DECRA Fellow, Dr. Spina does research in the field of Information Retrieval and Text Analytics (Data Science), focusing on interactive information retrieval and evaluation of information access systems.
Dr. Spina is the recipient of the 2021 RMIT Award for Research Impact (Technology).
Dr. Spina also participates in Capoeira and Samba.
- Senior Lecturer and DECRA Fellow at RMIT University, School of Computing Technologies.
- Member of the RMIT Research Centre for Information Discovery and Data Analytics (CIDDA).
- Associate Investigator at the ARC Centre of Excellence for Automated Decision-Making and Society.
- Editorial board member for the journals Elsevier Information Processing & Management (IP&M) and MDPI Information.
- Program Committee member for several conferences including The Web Conference, SIGIR, NAACL, CIKM, ECIR, EMNLP, and CHIIR, among others.
- Reviewer for renowned journals including Elsevier IP&M, ACM TOIS, EPJ Data Science, ACM TIST, and JASIST, among others.
- Data scientist at Signal AI in London, UK (2014-2015).
- Dr. Spina has collaborated in research with colleagues in different industry organisations such as Microsoft, Google, ABC, SEEK, Real Thing, Inference Solutions, Abbrevi8, and Llorente&Cuenca.
- Dr. Spina is the co-author of a patent on a system and method for automating the training of enterprise customer response systems using a range of dynamic or generic data sets.
- Al Bahem, A.,Spina, D.,Scholer, F.,Cavedon, L. (2022). Component-based Analysis of Dynamic Search Performance In: ACM Transactions on Information Systems, 40, 1 - 47
- Le, B.,Spina, D.,Scholer, F.,Chia, H. (2022). A Crowdsourcing Methodology to Measure Algorithmic Bias in Black-Box Systems: A Case Study with COVID-Related Searches In: Revised Selected Papers of the 3rd International Workshop on Advances in Bias and Fairness in Information Retrieval (BIAS 2022), Stavanger, Norway, 10 April 2022
- Trippas, J.,Spina, D.,Sanderson, M.,Cavedon, L. (2021). Accessing Media Via an Audio-only Communication Channel: A Log Analysis In: Proceedings of the 3rd Conference on Conversational User Interfaces (CUI 2021), Bilbao, Spain, 27-29 July 2021
- Kiesel, J.,Spina, D.,Wachsmuth, H.,Stein, B. (2021). The Meant, the Said, and the Understood: Conversational Argument Search and Cognitive Biases In: Proceedings of the 3rd Conference on Conversational User Interfaces, Online, 27-29 July 2021
- Saling, L.,Mallal, D.,Scholer, F.,Skelton, R.,Spina, D. (2021). No one is immune to misinformation: An investigation of misinformation sharing by subscribers to a fact-checking newsletter In: PLOS ONE, 16, 1 - 13
- Soprano, M.,Roitero, K.,Barbera, D.,Ceolin, D.,Spina, D.,Mizzaro, S.,Demartini, G. (2021). The many dimensions of truthfulness: Crowdsourcing misinformation assessments on a multidimensional scale In: Information Processing & Management, 58, 1 - 22
- Roitero, K.,Soprano, M.,Portelli, B.,De Luise, M.,Spina, D.,Della Mea, V.,Serra, G.,Mizzaro, S.,Demartini, G. (2021). (In Press) Can the crowd judge truthfulness? A longitudinal study on recent misinformation about COVID-19 In: Personal and Ubiquitous Computing, , 1 - 31
- Pathiyan Cherumanal, S.,Spina, D.,Scholer, F.,Croft, B. (2021). Evaluating Fairness in Argument Retrieval In: Proceedings of the 30th ACM International Conference on Information & Knowledge Management (CIKM 2021), Queensland, Australia, 1-5 November 2021
- Steiner, M.,Spina, D.,Scholer, F.,Cavedon, L. (2021). Crowdsourcing Backstories for Complex Task-Based Search In: Proceedings of the 25th Australasian Document Computing Symposium (ADCS 2021), Virtual Event, Australia, 9 December 2021
- Cerone, A.,Naghizade, E.,Scholer, F.,Mallal, D.,Skelton, R.,Spina, D. (2020). Watch ’n’ Check: Towards a Social Media Monitoring Tool to Assist Fact-Checking Experts In: Proceedings of the IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA 2020), Sydney, Australia, 6-9 October 2020
2 PhD Completions3 PhD Current Supervisions
- Fair and Transparent Information Access in Spoken Conversational Assistants. Funded by: ARC Discovery Early Career Researcher Award (DECRA) 2020 from (2020 to 2023)
- Training intelligent virtual agents using human interaction data (phase 2). Funded by: DIIS - Innovations Connections - Competitive from (2020 to 2020)
- Training intelligent virtual agents using human interaction data. Funded by: DIIS - Innovations Connections - Competitive from (2019 to 2020)
- A Learning to Rank Framework for Entity Centrality. Funded by: Abbrevi8 Grant 2016 from (2016 to 2016)