Caracterização do debate online sobre cigarro eletrônico no Brasil: Uma análise de tópicos de discussão no YouTube
Resumo
O YouTube é uma das plataformas de compartilhamento de vídeo mais populares do Brasil, proporcionando um ambiente dinâmico para a disseminação de opiniões sobre questões de saúde pública, incluindo os cigarros eletrônicos. Neste contexto, este estudo tem como objetivo caracterizar o debate online sobre cigarros eletrônicos no Brasil, empregando técnicas de processamento de linguagem natural para a análise de tópicos nos comentários de vídeos do YouTube relacionados ao tema. A coleção de dados utilizada abrange mais de 285 mil comentários sobre o tema, cobrindo o período entre o início de 2018 e o fim de 2023. Nossos resultados identificam múltiplos tópicos de discussão, refletindo uma dinâmica diversificada ao longo dos anos. A discussão é polarizada, com predominância de tópicos favoráveis ao uso de cigarros eletrônicos, mas também revela fortes divergências sobre questões de saúde e regulamentação.Referências
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ANVISA (2024). Anvisa atualiza regulação de cigarro eletrônico e mantém proibição. [link]. Acesso em: 21 de maio de 2024.
Campello, R. J., Moulavi, D., and Sander, J. (2013). Density-based clustering based on hierarchical density estimates. In Pacific-Asia conference on knowledge discovery and data mining, pages 160–172. Springer.
Campus, B., Fafard, P., Pierre, J. S., and Hoffman, S. J. (2021). Comparing the regulation and incentivization of e-cigarettes across 97 countries. Social science & medicine, 291:114187.
Capellaro, L. and Caseli, H. (2021). Análise de polaridade e de tópicos em tweets no domínio da política no brasil. In Anais do XIII Simpósio Brasileiro de Tecnologia da Informação e da Linguagem Humana, pages 47–55, Porto Alegre, RS, Brasil. SBC.
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da Rosa Jr, J. M., Linhares, R. S., Ferreira, C. H. G., Nobre, G. P., Murai, F., and Almeida, J. M. (2022). Uncovering discussion groups on claims of election fraud from twitter. In International conference on social informatics, pages 320–336. Springer.
Dashtian, H., Murthy, D., and Kong, G. (2022). An exploration of e-cigarette–related search items on youtube: network analysis. Journal of Medical Internet Research, 24(1):e30679.
Dias, A., Tanure, R. R., Almeida, J., Lima, H., and Ferreira, C. (2024). Análise da percepção do uso de cigarros eletrônicos no brasil por meio de comentários no youtube. In Brazilian Symposium on Multimedia and the Web.
Donaldson, S. I., Dormanesh, A., Perez, C., Zaffer, M. O., Majmundar, A., Unger, J. B., and Allem, J.-P. (2023). Monitoring the official youtube channels of e-cigarette companies: a thematic analysis. Health Education & Behavior, 50(5):677–682.
Grootendorst, M. (2022). Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794.
Hussein, E., Juneja, P., and Mitra, T. (2020). Measuring misinformation in video search platforms: An audit study on youtube. Proceedings of the ACM on Human-Computer Interaction, 4(CSCW1):1–27.
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Kong, G., LaVallee, H., Rams, A., Ramamurthi, D., and Krishnan-Sarin, S. (2019). Promotion of vape tricks on youtube: Content analysis. Journal of medical Internet research, 21(6):e12709.
Linhares, R. S., Rosa, J. M., Ferreira, C. H., Murai, F., Nobre, G., and Almeida, J. (2022). Uncovering coordinated communities on twitter during the 2020 us election. In 2022 IEEE/ACM international conference on advances in social networks analysis and mining (ASONAM), pages 80–87. IEEE.
Liu, Q. and Mengoni, P. (2023). What do airbnb users care about before, during and after the covid-19? an analysis of online reviews. In 2023 IEEE International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT), pages 409–414. IEEE.
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Malik, A., Khan, M. I., Karbasian, H., Nieminen, M., Ammad-Ud-Din, M., and Khan, S. A. (2021). Modeling public sentiments about juul flavors on twitter through machine learning. Nicotine and Tobacco Research, 23(11):1869–1879.
McInnes, L., Healy, J., Saul, N., and Großberger, L. (2018). Umap: Uniform manifold approximation and projection. Journal of Open Source Software, 3(29):861.
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Oncology, The Lancet (2013). Time for e-cigarette regulation. [link]. Acesso em: 30 de agosto de 2024.
Pereira, P. and da Silva, T. (2023). Uso de modelagem de tópicos para agrupamento de notícias: uma abordagem usando bertopic. In Anais do XIV Simpósio Brasileiro de Tecnologia da Informação e da Linguagem Humana, pages 398–402, Porto Alegre, RS, Brasil. SBC.
Porreca, A., Scozzari, F., and Di Nicola, M. (2020). Using text mining and sentiment analysis to analyse youtube italian videos concerning vaccination. BMC Public Health, 20:1–9.
Sari Kaunang, C. P., Amastini, F., and Mahendra, R. (2021). Analyzing stance and topic of e-cigarette conversations on twitter: Case study in indonesia. In 2021 IEEE 11th Annual Computing and Communication Workshop and Conference (CCWC).
Shah, N., Nali, M., Bardier, C., Li, J., Maroulis, J., Cuomo, R., and Mackey, T. K. (2023). Applying topic modelling and qualitative content analysis to identify and characterise ends product promotion and sales on instagram. Tobacco control, 32(e2):e153–e159.
Souza, F., Nogueira, R., and Lotufo, R. (2020). Bertimbau: pretrained bert models for brazilian portuguese. In Intelligent Systems: 9th Brazilian Conference, BRACIS 2020, Rio Grande, Brazil, October 20–23, 2020, Proceedings, Part I 9, pages 403–417. Springer.
Su, C. (2023). Douyin, TikTok and China’s online screen industry: The rise of short-video platforms. Taylor & Francis.
Turenne, N. (2023). Net activism and whistleblowing on youtube: a text mining analysis. Multimedia Tools and Applications, 82(6):9201–9221.
Vassey, J., Kennedy, C. J., Herbert Chang, H.-C., Smith, A. S., and Unger, J. B. (2023). Scalable Surveillance of E-Cigarette Products on Instagram and TikTok Using Computer Vision. Nicotine & Tobacco Research, 26(5):552–560.
Venâncio, O. R., Ferreira, C. H., Almeida, J. M., and da Silva, A. P. C. (2024a). Unraveling user coordination on telegram: A comprehensive analysis of political mobilization during the 2022 brazilian presidential election. In Proceedings of the International AAAI Conference on Web and Social Media, volume 18, pages 1545–1556.
Venâncio, O. R., Gonçalves, G. H., Ferreira, C. H., and da Silva, A. P. C. (2024b). Evidências de disseminação de conteúdo no telegram durante o ataque aos órgãos públicos brasileiros em 2023. In Brazilian Symposium on Multimedia and the Web (WebMedia), pages 385–389. SBC.
WHO (2023). World health organization report on the global tobacco epidemic, 2023: protect people from tobacco smoke. [link].
Wu, D., Kasson, E., Singh, A. K., Ren, Y., Kaiser, N., Huang, M., and Cavazos-Rehg, P. A. (2022). Topics and sentiment surrounding vaping on twitter and reddit during the 2019 e-cigarette and vaping use–associated lung injury outbreak: Comparative study. Journal of medical Internet research, 24(12):e39460.
Zhan, Y., Liu, R., Li, Q., Leischow, S. J., and Zeng, D. D. (2017). Identifying topics for e-cigarette user-generated contents: a case study from multiple social media platforms. Journal of medical Internet research, 19(1):e24.
ANVISA (2024). Anvisa atualiza regulação de cigarro eletrônico e mantém proibição. [link]. Acesso em: 21 de maio de 2024.
Campello, R. J., Moulavi, D., and Sander, J. (2013). Density-based clustering based on hierarchical density estimates. In Pacific-Asia conference on knowledge discovery and data mining, pages 160–172. Springer.
Campus, B., Fafard, P., Pierre, J. S., and Hoffman, S. J. (2021). Comparing the regulation and incentivization of e-cigarettes across 97 countries. Social science & medicine, 291:114187.
Capellaro, L. and Caseli, H. (2021). Análise de polaridade e de tópicos em tweets no domínio da política no brasil. In Anais do XIII Simpósio Brasileiro de Tecnologia da Informação e da Linguagem Humana, pages 47–55, Porto Alegre, RS, Brasil. SBC.
Chen, L., Lu, X., Yuan, J., Luo, J., Luo, J., Xie, Z., and Li, D. (2020). A social media study on the associations of flavored electronic cigarettes with health symptoms: observational study. Journal of medical Internet research, 22(6):e17496.
Costa, J., Oliveira, G., Fonseca, G., Reis, D., Teixeira, G., Cunha, W., Rocha, L., and Ferreira, C. H. (2025). Characterizing youtube’s role in online gambling promotion: A case study of fortune tiger in brazil. In Proceedings of the 17th ACM Web Science Conference 2025.
da Rosa Jr, J. M., Linhares, R. S., Ferreira, C. H. G., Nobre, G. P., Murai, F., and Almeida, J. M. (2022). Uncovering discussion groups on claims of election fraud from twitter. In International conference on social informatics, pages 320–336. Springer.
Dashtian, H., Murthy, D., and Kong, G. (2022). An exploration of e-cigarette–related search items on youtube: network analysis. Journal of Medical Internet Research, 24(1):e30679.
Dias, A., Tanure, R. R., Almeida, J., Lima, H., and Ferreira, C. (2024). Análise da percepção do uso de cigarros eletrônicos no brasil por meio de comentários no youtube. In Brazilian Symposium on Multimedia and the Web.
Donaldson, S. I., Dormanesh, A., Perez, C., Zaffer, M. O., Majmundar, A., Unger, J. B., and Allem, J.-P. (2023). Monitoring the official youtube channels of e-cigarette companies: a thematic analysis. Health Education & Behavior, 50(5):677–682.
Grootendorst, M. (2022). Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794.
Hussein, E., Juneja, P., and Mitra, T. (2020). Measuring misinformation in video search platforms: An audit study on youtube. Proceedings of the ACM on Human-Computer Interaction, 4(CSCW1):1–27.
IBOPE (2023). Video audience share percentage in brazil. [link].
Kong, G., LaVallee, H., Rams, A., Ramamurthi, D., and Krishnan-Sarin, S. (2019). Promotion of vape tricks on youtube: Content analysis. Journal of medical Internet research, 21(6):e12709.
Linhares, R. S., Rosa, J. M., Ferreira, C. H., Murai, F., Nobre, G., and Almeida, J. (2022). Uncovering coordinated communities on twitter during the 2020 us election. In 2022 IEEE/ACM international conference on advances in social networks analysis and mining (ASONAM), pages 80–87. IEEE.
Liu, Q. and Mengoni, P. (2023). What do airbnb users care about before, during and after the covid-19? an analysis of online reviews. In 2023 IEEE International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT), pages 409–414. IEEE.
Lyu, J. C., Luli, G. K., and Ling, P. M. (2021). Vaping discussion in the covid-19 pandemic: An observational study using twitter data. PloS one, 16(12):e0260290.
Ma, B., Wong, Y. D., Teo, C.-C., and Wang, Z. (2024). Enhance understandings of online food delivery’s service quality with online reviews. Journal of retailing and consumer services, 76:103588.
Malik, A., Khan, M. I., Karbasian, H., Nieminen, M., Ammad-Ud-Din, M., and Khan, S. A. (2021). Modeling public sentiments about juul flavors on twitter through machine learning. Nicotine and Tobacco Research, 23(11):1869–1879.
McInnes, L., Healy, J., Saul, N., and Großberger, L. (2018). Umap: Uniform manifold approximation and projection. Journal of Open Source Software, 3(29):861.
Murthy, D., Lee, J., Dashtian, H., Kong, G., et al. (2023). Influence of user profile attributes on e-cigarette–related searches on youtube: Machine learning clustering and classification. JMIR infodemiology, 3(1):e42218.
Oncology, The Lancet (2013). Time for e-cigarette regulation. [link]. Acesso em: 30 de agosto de 2024.
Pereira, P. and da Silva, T. (2023). Uso de modelagem de tópicos para agrupamento de notícias: uma abordagem usando bertopic. In Anais do XIV Simpósio Brasileiro de Tecnologia da Informação e da Linguagem Humana, pages 398–402, Porto Alegre, RS, Brasil. SBC.
Porreca, A., Scozzari, F., and Di Nicola, M. (2020). Using text mining and sentiment analysis to analyse youtube italian videos concerning vaccination. BMC Public Health, 20:1–9.
Sari Kaunang, C. P., Amastini, F., and Mahendra, R. (2021). Analyzing stance and topic of e-cigarette conversations on twitter: Case study in indonesia. In 2021 IEEE 11th Annual Computing and Communication Workshop and Conference (CCWC).
Shah, N., Nali, M., Bardier, C., Li, J., Maroulis, J., Cuomo, R., and Mackey, T. K. (2023). Applying topic modelling and qualitative content analysis to identify and characterise ends product promotion and sales on instagram. Tobacco control, 32(e2):e153–e159.
Souza, F., Nogueira, R., and Lotufo, R. (2020). Bertimbau: pretrained bert models for brazilian portuguese. In Intelligent Systems: 9th Brazilian Conference, BRACIS 2020, Rio Grande, Brazil, October 20–23, 2020, Proceedings, Part I 9, pages 403–417. Springer.
Su, C. (2023). Douyin, TikTok and China’s online screen industry: The rise of short-video platforms. Taylor & Francis.
Turenne, N. (2023). Net activism and whistleblowing on youtube: a text mining analysis. Multimedia Tools and Applications, 82(6):9201–9221.
Vassey, J., Kennedy, C. J., Herbert Chang, H.-C., Smith, A. S., and Unger, J. B. (2023). Scalable Surveillance of E-Cigarette Products on Instagram and TikTok Using Computer Vision. Nicotine & Tobacco Research, 26(5):552–560.
Venâncio, O. R., Ferreira, C. H., Almeida, J. M., and da Silva, A. P. C. (2024a). Unraveling user coordination on telegram: A comprehensive analysis of political mobilization during the 2022 brazilian presidential election. In Proceedings of the International AAAI Conference on Web and Social Media, volume 18, pages 1545–1556.
Venâncio, O. R., Gonçalves, G. H., Ferreira, C. H., and da Silva, A. P. C. (2024b). Evidências de disseminação de conteúdo no telegram durante o ataque aos órgãos públicos brasileiros em 2023. In Brazilian Symposium on Multimedia and the Web (WebMedia), pages 385–389. SBC.
WHO (2023). World health organization report on the global tobacco epidemic, 2023: protect people from tobacco smoke. [link].
Wu, D., Kasson, E., Singh, A. K., Ren, Y., Kaiser, N., Huang, M., and Cavazos-Rehg, P. A. (2022). Topics and sentiment surrounding vaping on twitter and reddit during the 2019 e-cigarette and vaping use–associated lung injury outbreak: Comparative study. Journal of medical Internet research, 24(12):e39460.
Zhan, Y., Liu, R., Li, Q., Leischow, S. J., and Zeng, D. D. (2017). Identifying topics for e-cigarette user-generated contents: a case study from multiple social media platforms. Journal of medical Internet research, 19(1):e24.
Publicado
20/07/2025
Como Citar
TANURE, Richardy R.; DIAS, Aline M.; CAMELO, Lucas A.; ALMEIDA, Jussara; LIMA, Helen C. S. C.; FERREIRA, Carlos H. G..
Caracterização do debate online sobre cigarro eletrônico no Brasil: Uma análise de tópicos de discussão no YouTube. In: BRAZILIAN WORKSHOP ON SOCIAL NETWORK ANALYSIS AND MINING (BRASNAM), 14. , 2025, Maceió/AL.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 54-64.
ISSN 2595-6094.
DOI: https://doi.org/10.5753/brasnam.2025.8544.
