MIPS - Mapping the Relationship between Research, Innovation, and Society through Topic Modeling
Resumo
The growing proliferation of data on the Web and in multimedia systems, including scientific articles, patents, and social media discussions, covers the entire innovation lifecycle. However, the integrated analysis of these data to guide strategic decisions remains a challenge, given its heterogeneous and fragmented nature. The thesis proposes an integrated technique to identify, analyze, and map the relationships between topics present in the scientific, technological, and social dimensions over time. Using topic modeling, the solution reveals how innovation is born in research, transforms into technology, and is perceived by society, offering a multidimensional view that current solutions do not provide. Experiments demonstrate the method’s ability to track the evolution of research themes and their social discussion, including the identification of rumors and disinformation. The results indicate significant connections between the dimensions, highlighting the approach’s utility for technological foresight and impact analysis.Referências
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Cerri, A. and Nolasco, D. (2023). KNOWLEDGE GRAPH FOR COVID-19: A FUSION APPROACH - SIAC UFRJ, pages 385–385.
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Kullback, S. and Leibler, R. A. (1951). On information and sufficiency. The Annals of Mathematical Statistics, 22(1):79–86.
Li, X., Xie, Q., and Huang, L. (2022). Identifying the development trends of emerging technologies using patent analysis and web news data mining: The case of perovskite solar cell technology. IEEE Transactions on Engineering Management, 69:2603–2618.
Liu, Y., Wang, J., Qian, Y., Jiang, Y., Sun, J., and Chai, Y. (2021). Dynamic topic model for tracking topic evolution and measuring popularity of scientific literature. In 2021 IEEE Sixth International Conference on Data Science in Cyberspace (DSC), pages 315–320.
Machado, L. B., Moresi, E. A. D., Ferneda, E., and Prado, H. A. D. (2023). Bib-ana: a tool to detect emerging technologies through topic modeling and generative artificial intelligence. In 2023 18th Iberian Conference on Information Systems and Technologies (CISTI), pages 1–6.
Maskittou, M., Haddadi, A. E. L., and Routaib, H. (2022). Intelligent technology management based on patent topic modeling. In 2022 5th International Conference on Networking, Information Systems and Security: Envisage Intelligent Systems in 5g//6G-based Interconnected Digital Worlds (NISS), pages 1–4.
Nolasco, D. and Oliveira, J. (2018a). Intelligent subevent detection based on social network data. In Proceedings - 2017 IEEE 15th International Conference on Dependable, Autonomic and Secure Computing, volume 2018-January.
Nolasco, D. and Oliveira, J. (2018b). Publish or post: Identification of influences between science and society through intelligent systems. ceur-ws.orgD Nolasco, J OliveiraBiDu-Posters@ VLDB, 2018•ceur-ws.org.
Nolasco, D. and Oliveira, J. (2019). Subevents detection through topic modeling in social media posts. Future Generation Computer Systems, 93:290–303.
Nolasco, D. and Oliveira, J. (2020a). Mining social influence in science and vice-versa: A topic correlation approach. International Journal of Information Management, 51.
Nolasco, D. and Oliveira, J. (2020b). A study of rumor detection based on social network topic models relationship. In sol.sbc.org.br, pages 166–177.
Nolasco, D. and Oliveira, J. (2021). Topical rumor detection based on social network topic models relationship. sol.sbc.org.brD Nolasco, J OliveiraiSys-Brazilian Journal of Information Systems, 2021•sol.sbc.org.br, 14:5–27.
Nolasco, D. and Oliveira, J. (2025). Mirabr – a system for patent analysis. Simpósio Brasileiro de Sistemas de Informação (SBSI), pages 838–845.
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., McGuinness, L. A., Stewart, L. A., Thomas, J., Tricco, A. C., Welch, V. A., Whiting, P., and Moher, D. (2021). The prisma 2020 statement: an updated guideline for reporting systematic reviews. BMJ, 372.
Parsons, S. and Khuri, N. (2020). Discovery of research trends in computer science education on ethics using topic modeling. In 2020 International Conference on Computational Science and Computational Intelligence (CSCI), pages 885–891.
Smeaton, A. F., Keogh, G., Gurrin, C., McDonald, K., and Sodring, T. (2003). Analysis of papers from twenty-five years of sigir conferences: what have we been doing for the last quarter of a century? In SIGIR Forum, volume 37, pages 49–53.
Vijaymeena, M. and Kavitha, K. (2016). A survey on similarity measures in text mining. Machine Learning and Applications: An International Journal, 3(2):19–28.
World Health Organization (2016). Who – dispelling rumours around Zika and complications. [link]. Accessed: 2026-02-23.
Boscarioli, C., Araujo, R. M., and Maciel, R. S. P., editors (2017). I GranDSI-BR – Grand Research Challenges in Information Systems in Brazil 2016–2026. Brazilian Computer Society (SBC), Special Committee on Information Systems (CE-SI). ISBN 978-85-7669-384-0.
Cerri, A. and Nolasco, D. (2023). KNOWLEDGE GRAPH FOR COVID-19: A FUSION APPROACH - SIAC UFRJ, pages 385–385.
Cerri, A., Nolasco, D., and Oliveira, J. (2022). Integração de dados científicos sobre Covid-19 através de Grafos de Conhecimento - SIAC UFRJ, pages 401–401.
de S. Costa, G., Couto, D. C. C., Junior, A. F. L. J., and Lobato, F. M. F. (2022). Feminismo e redes sociais online: uma análise de tweets sobre o dia internacional da mulher. Brazilian Workshop on Social Network Analysis and Mining (BraSNAM), pages 169–180.
de Sousa, A. M. and Becker, K. (2022). Comparando os posicionamentos a favor/contra a vacinação covid nos estados unidos da américa e no brasil. Simpósio Brasileiro de Banco de Dados (SBBD), pages 65–77.
Dresch, A., Lacerda, D. P., Jr, J. A. V. A., Dresch, A., Lacerda, D. P., and Antunes, J. A. V. (2015). Design Science Research, pages 67–102. Springer International Publishing.
Falciola, L. and Barbieri, M. (2022). Searching and analyzing patent-relevant covid-19 information. World Patent Information, 68:102094.
Kauer, V. A. (2013). Evolução dos temas de interesse do sbbd ao longo dos anos. Simpósio Brasileiro de Banco de Dados, pages 1–6.
Kullback, S. and Leibler, R. A. (1951). On information and sufficiency. The Annals of Mathematical Statistics, 22(1):79–86.
Li, X., Xie, Q., and Huang, L. (2022). Identifying the development trends of emerging technologies using patent analysis and web news data mining: The case of perovskite solar cell technology. IEEE Transactions on Engineering Management, 69:2603–2618.
Liu, Y., Wang, J., Qian, Y., Jiang, Y., Sun, J., and Chai, Y. (2021). Dynamic topic model for tracking topic evolution and measuring popularity of scientific literature. In 2021 IEEE Sixth International Conference on Data Science in Cyberspace (DSC), pages 315–320.
Machado, L. B., Moresi, E. A. D., Ferneda, E., and Prado, H. A. D. (2023). Bib-ana: a tool to detect emerging technologies through topic modeling and generative artificial intelligence. In 2023 18th Iberian Conference on Information Systems and Technologies (CISTI), pages 1–6.
Maskittou, M., Haddadi, A. E. L., and Routaib, H. (2022). Intelligent technology management based on patent topic modeling. In 2022 5th International Conference on Networking, Information Systems and Security: Envisage Intelligent Systems in 5g//6G-based Interconnected Digital Worlds (NISS), pages 1–4.
Nolasco, D. and Oliveira, J. (2018a). Intelligent subevent detection based on social network data. In Proceedings - 2017 IEEE 15th International Conference on Dependable, Autonomic and Secure Computing, volume 2018-January.
Nolasco, D. and Oliveira, J. (2018b). Publish or post: Identification of influences between science and society through intelligent systems. ceur-ws.orgD Nolasco, J OliveiraBiDu-Posters@ VLDB, 2018•ceur-ws.org.
Nolasco, D. and Oliveira, J. (2019). Subevents detection through topic modeling in social media posts. Future Generation Computer Systems, 93:290–303.
Nolasco, D. and Oliveira, J. (2020a). Mining social influence in science and vice-versa: A topic correlation approach. International Journal of Information Management, 51.
Nolasco, D. and Oliveira, J. (2020b). A study of rumor detection based on social network topic models relationship. In sol.sbc.org.br, pages 166–177.
Nolasco, D. and Oliveira, J. (2021). Topical rumor detection based on social network topic models relationship. sol.sbc.org.brD Nolasco, J OliveiraiSys-Brazilian Journal of Information Systems, 2021•sol.sbc.org.br, 14:5–27.
Nolasco, D. and Oliveira, J. (2025). Mirabr – a system for patent analysis. Simpósio Brasileiro de Sistemas de Informação (SBSI), pages 838–845.
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., McGuinness, L. A., Stewart, L. A., Thomas, J., Tricco, A. C., Welch, V. A., Whiting, P., and Moher, D. (2021). The prisma 2020 statement: an updated guideline for reporting systematic reviews. BMJ, 372.
Parsons, S. and Khuri, N. (2020). Discovery of research trends in computer science education on ethics using topic modeling. In 2020 International Conference on Computational Science and Computational Intelligence (CSCI), pages 885–891.
Smeaton, A. F., Keogh, G., Gurrin, C., McDonald, K., and Sodring, T. (2003). Analysis of papers from twenty-five years of sigir conferences: what have we been doing for the last quarter of a century? In SIGIR Forum, volume 37, pages 49–53.
Vijaymeena, M. and Kavitha, K. (2016). A survey on similarity measures in text mining. Machine Learning and Applications: An International Journal, 3(2):19–28.
World Health Organization (2016). Who – dispelling rumours around Zika and complications. [link]. Accessed: 2026-02-23.
Publicado
25/05/2026
Como Citar
NOLASCO, Diogo; OLIVEIRA, Jonice.
MIPS - Mapping the Relationship between Research, Innovation, and Society through Topic Modeling. In: CONCURSO DE TESES, DISSERTAÇÕES E TCCS EM SI - DOUTORADO - SIMPÓSIO BRASILEIRO DE SISTEMAS DE INFORMAÇÃO (SBSI), 22. , 2026, Vitória/ES.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2026
.
p. 7-22.
DOI: https://doi.org/10.5753/sbsi_estendido.2026.249127.
