Abstract
To effectively monitor academic progress towards United Nations Sustainable Development Goals (SDGs), accurate and up-to-date information is crucial. Thematic modeling with keyword-based queries have emerged as a promising tool for clustering the large amounts of publications available in academic research databases. However, the effectiveness of these bibliometric queries depends on the choice of keywords used, and there is no standardized set of keywords for tracking academic progress towards the SDGs. In this study, we performed a comparative analysis of the most used keyword-based queries, assessing their advantages and disadvantages, and identifying gaps and redundancies. Based on our findings, we performed a meta-based approach to develop a new set of keywords that is both concise and thematically representative.
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Notes
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We couldn’t retrieve the information for SDG4 on the ELS22 set. To avoid missing values we added the average of SDG4 between AUC and ELS20.
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Acknowledgements
The work presented in the current study was undertaken in the framework of the IBA-SwafS-Support-2-2020 project “EUt- EXTRAS: European University of Technology - Experimentation to Transform Research Activities and Steering” under the contract H2020-IBA-SwafS-Support-2-2020/101035812.
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Partaourides, H., Kouzaridi, E., Tsapatsoulis, N., Djouvas, C. (2023). Thematic Modeling of UN Sustainable Development Goals: A Comparative Meta-based Approach. In: Maglogiannis, I., Iliadis, L., MacIntyre, J., Dominguez, M. (eds) Artificial Intelligence Applications and Innovations. AIAI 2023. IFIP Advances in Information and Communication Technology, vol 676. Springer, Cham. https://doi.org/10.1007/978-3-031-34107-6_44
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