A Hybrid Approach to Fake News Detection: Machine Learning and Knowledge-Based Verification

• Caroline Barbosa de Oliveira Lira University of São Paulo (USP) http://orcid.org/0000-0001-5376-1071
• Kamila Rios da Hora Rodrigues University of São Paulo (USP) https://orcid.org/0000-0001-9176-8709

DOI: https://doi.org/10.5753/ihc.2025.10924

Abstract

Introduction: The spread of fake news has become a global problem. Events such as pandemics, armed conflicts, trade wars, and intensely polarized elections have transformed social media into channels ripe for manipulation, the spread of conspiracy theories, and the dissemination of protest and hate speech. Human vulnerabilities—notably cognitive vulnerabilities—are often exploited, especially in the case of older adults, to fuel the circulation of misinformation. Objective: This paper describes a proposed hybrid approach aimed at reducing the spread and sharing of fake news on social media. This approach utilizes accessible verification resources through a strategy initially based on knowledge dissemination, followed by the incorporation of specific tools for automatic fake news detection, based on machine learning techniques. Methodology: A corpus of news articles in Portuguese was previously refined, and tests were conducted to assess its accuracy. Results: The results demonstrated promising performance in terms of accuracy, leading to the implementation of this corpus in the ADA tool. This tool, designed to verify the veracity of news stories, was developed using Participatory Design (PD) techniques with the 60+ audience, aiming to promote engagement and encourage adoption of technology to help identify fake news. The results indicate a tool with good adoption among the target audience tested and with accessibility features.

Keywords: Fake News, Hybrid Approach, Knowledge-Based Verification, Machine Learning, ADA Tool

References

Abras, C., Maloney-Krichmar, D., Preece, J., et al. (2004). User-centered design. Bainbridge, W. Encyclopedia of Human-Computer Interaction. Thousand Oaks: Sage Publications, 37(4):445–456.

Baarir, N. F. e Djeffal, A. (2021). Fake news detection using machine learning. In 2020 2nd International Workshop on Human-Centric Smart Environments for Health and Well-being (IHSH), pages 125–130.

Barbosa, S. e Silva, B. (2010). Interação humano-computador. Elsevier Brasil.

Braddock, K. (2022). Vaccinating against hate: Using attitudinal inoculation to confer resistance to persuasion by extremist propaganda. Terrorism and political violence, 34(2):240–262.

Brashier, N. M. e Schacter, D. L. (2020). Aging in an era of fake news. Current directions in psychological science, 29(3):316–323. Disponível em: [link]. Acesso em: Maio de 2025.

Cooper, A., Reimann, R., Cronin, D., e Noessel, C. (2014). About face: the essentials of interaction design. John Wiley & Sons.

da Silva Junior, D. P., Alves, D. D., Carneiro, N., Matos, E. d. S., Baranauskas, M. C. C., e Mendoza, Y. L. M. (2024). Grandihc-br 2025-2035 - gc1: New theoretical and methodological approaches in hci. In Proceedings of the XXIII Brazilian Symposium on Human Factors in Computing Systems, IHC ’24, New York, NY, USA. Association for Computing Machinery.

Della Vedova, M. L., Tacchini, E., Moret, S., Ballarin, G., Di Pierro, M., e De Alfaro, L. (2018). Automatic online fake news detection combining content and social signals. In 22nd Conference of Open Innovations Association (FRUCT), pages 272–279. IEEE.

Faceli, K., Lorena, A. C., Gama, J., Almeida, T. A. d., e Carvalho, A. C. P. d. L. F. d. (2021). Inteligência artifcial: uma abordagem de aprendizado de máquina. LTC. Disponível em: [link]. Acesso em: Maio 2025.

Galvão, M. C. T. (2023). O uso das ferramentas de comunicação instantânea na comunicação interna de uma instituição: um estudo na ufrn. Disponível em: [link]. Acesso em: Maio 2025.

Gaspar, R. d. P., Bonacin, R., e Gonçalves, V. (2021). Um estudo sobre atividades participativas para soluções iot para o home care de pessoas idosas. arXiv preprint arXiv:2103.01078. Disponível em: [link]. Acesso em maio 2025.

Guess, A. M., Lerner, M., Lyons, B., Montgomery, J. M., Nyhan, B., Reifer, J., e Sircar, N. (2020). A digital media literacy intervention increases discernment between mainstream and false news in the united states and india. Proceedings of the National Academy of Sciences, 117(27):15536–15545.

Jouhar, J., Pratap, A., Tijo, N., e Mony, M. (2024). Fake news detection using python and machine learning. Procedia Computer Science, 233:763–771.

Kong, S. H., Tan, L. M., Gan, K. H., e Samsudin, N. H. (2020). Fake news detection using deep learning. In 2020 IEEE 10th symposium on computer applications & industrial electronics (ISCAIE), pages 102–107. IEEE.

Lira, C. e Rodrigues, K. (2023). Ada - ferramenta para detecção automática de notícias falsas. In Anais Estendidos do XXII Simpósio Brasileiro sobre Fatores Humanos em Sistemas Computacionais, pages 160–164, Porto Alegre, RS, Brasil. SBC.

Lowdermilk, T. (2019). Design centrado no usuário: um guia para o desenvolvimento de aplicativos amigáveis. Novatec Editora.

Manning, C. D., Raghavan, P., e Schütze, H. (2008). Introduction to information retrieval, volume 39. Cambridge University Press, Cambridge.

Melo, A. e Abelheira, R. (2015). Design Thinking & Thinking Design: Metodologia, ferramentas e uma refexão sobre o tema. Novatec Editora.

Monteiro, R. A., Santos, R. L. S., Pardo, T. A. S., Almeida, T. A., Ruiz, E. E. S., e Vale, O. A. (2018). Contributions to the study of fake news in portuguese: New corpus and automatic detection results. In International Conference on Computational Processing of the Portuguese Language, pages 324–334. Springer.

Moreira Filho, J. L. (2021). Python para linguística de corpus: guia prático. Ilexis.net.it Editora.

Neris, V. P. A., Rosa, J. C. S., Maciel, C., Pereira, V. C., Galvão, V. F., e Arruda, I. L. (2024). Grandihc-br 2025-2035 - gc4: Sociocultural aspects in human-computer interaction. In Proceedings of the XXIII Brazilian Symposium on Human Factors in Computing Systems, IHC ’24, New York, NY, USA. Association for Computing Machinery.

Ng, A. W., Lo, H. W., e Chan, A. H. (2011). Measuring the usability of safety signs: A use of system usability scale (sus). In proceedings of the International MultiConference of Engineers and Computer Scientists, volume 2, pages 1296–1301. IAENG Hong Kong. Disponível em: [link]. Acesso em: maio 2025.

Rogers, Y., Sharp, H., e Preece, J. (2013). Design de interação. Bookman Editora.

Santos, R. L. d. S. (2022). Detecção automática de notícias falsas em português. PhD thesis, Universidade de São Paulo. Disponível em: [link]. Acesso em maio 2025.

Talwar, S., Dhir, A., Singh, D., Virk, G. S., e Salo, J. (2020). Sharing of fake news on social media: Application of the honeycomb framework and the third-person effect hypothesis. Journal of Retailing and Consumer Services, 57:102197.

Van der Linden, S., Roozenbeek, J., et al. (2020). Psychological inoculation against fake news. The psychology of fake news: Accepting, sharing, and correcting misinformation, pages 147–169.

Zervopoulos, A., Alvanou, A. G., Bezas, K., Papamichail, A., Maragoudakis, M., e Kermanidis, K. (2020). Hong kong protests: using natural language processing for fake news detection on twitter. In IFIP international conference on artificial intelligence applications and innovations, pages 408–419. Springer.