GoViz
A Visualization Tool for Empowering Transparency in Government Speech
DOI:
https://doi.org/10.59490/dgo.2025.954Keywords:
natural language processing, data visualisation, digital governmentAbstract
Public speech from government figures often describes relevant actions that can impact the population’s lives. However, most people do not have time and access to analyze and understand public speech. Such a scenario narrows the participation of the people in the main discussions, which leads to multiple misunderstandings. In this work, we propose GoViz, a tool that automatically produces visual representations to outline governmental speeches regarding the subject, its main actors, and how they connect to the discussion topics. GoViz processes natural language from speech transcriptions in a pipeline that identifies part-of-speech elements, named-entities, and the relation between persons, making speech content more accessible and insightful. Using publicly available data, we evaluate our tool in two different languages (Portuguese and English). The results demonstrate that the visualizations from both data facilitate understanding the speech content. Thus, our main contribution is to encourage the participation of citizens in parliamentary issues, allowing a simplified and visually engaging avenue to access long speeches and fostering improved communication between parliamentarians and the population.
Downloads
References
Bernardes, C. B., & Bandeira, C. L. (2016). Information vs Engagement in parliamentary websites – a case study of Brazil and the UK. Revista de Sociologia e Política, 24(59), 91–107. DOI: https://doi.org/10.1590/1678-987316245905.
Bönisch, K., Abrami, G., Wehnert, S., & Mehler, A. (2023). Bundestag-mine: Natural language processing for extracting key information from government documents. Frontiers in Artificial Intelligence and Applications, 379, 391–394. DOI: https://doi.org/10.3233/FAIA230996.
Cantador, I., Viejo-Tardío, J., Cortés-Cediel, M. E., & Rodríguez Bolívar, M. P. (2021). A chatbot for searching and exploring open data: Implementation and evaluation in e-government. DG.O2021: The 22nd Annual International Conference on Digital Government Research, 168–179. DOI: https://doi.org/10.1145/3463677.3463681.
Diaz, G. A., Chesñevar, C. I., Estevez, E., & Maguitman, A. (2022). Stance trees: A novel approach for assessing politically polarized issues in twitter. Proceedings of the 15th International Conference on Theory and Practice of Electronic Governance, 19–24. DOI: https://doi.org/10.1145/3560107.3560296.
Harrison, T. M., Guerrero, S., Burke, G. B., Cook, M., Cresswell, A., Helbig, N., Hrdinová, J., & Pardo, T. (2011). Open government and e-government: Democratic challenges from a public value perspective. Proceedings of the 12th Annual International Digital Government Research Conference: Digital Government Innovation in Challenging Times, 245–253. DOI: https://doi.org/10.1145/2037556.2037597.
Heer, J., Bostock, M., & Ogievetsky, V. (2010). A tour through the visualization zoo: A survey of powerful visualization techniques, from the obvious to the obscure. Queue, 8(5), 20–30. DOI: https://doi.org/10.1145/1794514.1805128.
Hinton, G. E., & Roweis, S. (2002). Stochastic neighbor embedding. Advances in neural information processing systems, 15, 857–864.
Inter-Parliamentary Union. (2022). Global Parliamentary Report 2022 - Public engagement in the work of parliament (2022nd ed.) [publisher: Inter-Parliamentary Union].
Juvino Santos, L. R., Balby Marinho, L., & Calazans Campelo, C. E. (2022). Uniting politics and pandemic: A social network analysis on the covid parliamentary commission of inquiry in brazil. Proceedings of the Brazilian Symposium on Multimedia and the Web, 99–107. DOI: https://doi.org/10.1145/3539637.3556992.
Keim, D. A., & Oelke, D. (2007). Literature fingerprinting: A new method for visual literary analysis. 2007 IEEE Symposium on Visual Analytics Science and Technology, 115–122. DOI: https://doi.org/10.1109/VAST.2007.4389004.
Kessler, J. (2017). Scattertext: A browser-based tool for visualizing how corpora differ. Proceedings of ACL 2017, System Demonstrations, 85–90.
Khartabil, D., Collins, C., Wells, S., Bach, B., & Kennedy, J. (2021). Design and evaluation of visualization techniques to facilitate argument exploration. Computer Graphics Forum, 40(6), 447–465. DOI: https://doi.org/10.1111/cgf.14389.
Montani, I., Honnibal, M., Honnibal, M., Landeghem, S. V., Boyd, A., Peters, H., McCann, P. O., Samsonov, M., Geovedi, J., O’Regan, J., Altinok, D., Orosz, G., Kristiansen, S. L., de Kok, D., Miranda, L., Roman, Bot, E., Fiedler, L., Howard, G., … Böing, B. (2020, June). spaCy: Industrial-strength Natural Language Processing in Python. DOI: https://doi.org/10.5281/zenodo.6621076.
Palma, I., Ladeira, M., & Reis, A. C. B. (2021). Machine learning predictive model for the passive transparency at the brazilian ministry of mines and energy. DG.O2021: The 22nd Annual International Conference on Digital Government Research, 76–81. DOI: https://doi.org/10.1145/3463677.3463715.
Quintero-Angulo, R. A. D., Sánchez-Torres, J. M., & Cardona-Román, D. M. (2020). Problem areas in eparticipation: A systematic review. Proceedings of the 13th International Conference on Theory and Practice of Electronic Governance, 544–550. DOI: https://doi.org/10.1145/3428502.3428584.
Rajaraman, A., & Ullman, J. D. (2011). Data mining. In Mining of massive datasets (pp. 1–17). Cambridge University Press. DOI: https://doi.org/10.1017/CBO9781139058452.002.
Rauh, C., & Schwalbach, J. (2020). The ParlSpeech V2 data set: Full-text corpora of 6.3 million parliamentary speeches in the key legislative chambers of nine representative democracies. DOI: https://doi.org/10.7910/DVN/L4OAKN.
Rehurek, R., & Sojka, P. (2011). Gensim–python framework for vector space modelling. NLP Centre, Faculty of Informatics, Masaryk University, Brno, Czech Republic, 3(2), 45–50.
Reimers, N., & Gurevych, I. (2019). Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks. Proceedings of the Conference on Empirical Methods in Natural Language Processing, 3980–3990. [link]
Steinbauer, M., Hiesmair, M., & Anderst-Kotsis, G. (2016). Making computers understand coalition and opposition in parliamentary democracy. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9820 LNCS, 265–276. DOI: https://doi.org/10.1007/978-3-319-44421-5_21.
Van der Maaten, L., & Hinton, G. (2008). Visualizing data using t-sne. Journal of machine learning research, 9(11), 2579–2605.
Volkovskii, D., & Filatova, O. (2022). Agreement and disagreement in american social media discussions (evidence from facebook discussions on the second impeachment of d. trump). Proceedings of the 15th International Conference on Theory and Practice of Electronic Governance, 221–228. DOI: https://doi.org/10.1145/3560107.3560144.
Ward, M., Grinstein, G., & Keim, D. (2015). Interactive data visualization: Foundations, techniques, and applications, second edition. CRC Press.
Wattenberg, M., & Viégas, F. B. (2008). The word tree, an interactive visual concordance. IEEE Transactions on Visualization and Computer Graphics, 14(6), 1221–1228. DOI: https://doi.org/10.1109/TVCG.2008.172.
Downloads
Published
How to Cite
Conference Proceedings Volume
Section
License
Copyright (c) 2025 Larissa Guder, João Paulo Aires, Isabel H. Manssour, Dalvan Griebler
This work is licensed under a Creative Commons Attribution 4.0 International License.
