Feminismo e Redes Sociais Online: uma Análise de Tweets sobre o Dia Internacional da Mulher
Resumo
As redes sociais estão desempenhando um papel cada vez mais importante no suporte a discursos e agendas do movimento feminista atual. Visando identificar quais as temáticas abordadas pela agenda feminista ao redor do mundo e quais polaridades estão presentes nessas manifestações, este trabalho analisa dados coletados do Twitter relacionados ao Dia Internacional da Mulher. Para isso, foram aplicadas modelagem de tópicos e análise de sentimento. Os dados utilizados foram coletados em tempo real durante os dias anteriores e posteriores ao 8 de março nos anos de 2020 e 2021. Os resultados mostraram que as temáticas encontradas variam de um ano para o outro, mas todos estão confluentes com o movimento. E ainda, existem tópicos que sempre são abordados e que a polaridade em relação a essas manifestações tende a ser de maioria neutra.
Palavras-chave:
Feminismo, Modelagem de Tópicos, Análise de Sentimentos
Referências
Araújo, M., Pereira, A., and Benevenuto, F. (2020). A comparative study of machine translation for multilingual sentence-level sentiment analysis. Information Sciences, 512.
Araújo, M. L. D., Diniz, J. P., Bastos, L., Soares, E., Júnior, M., Ferreira, M., Ribeiro, F., and Benevenuto, F. (2016). ifeel 2.0: A multilingual benchmarking system for sentence-level sentiment analysis. In Tenth International AAAI Conference on Web and Social Media.
Belford, M., Mac Namee, B., and Greene, D. (2018). Stability of topic modeling via matrix factorization. Expert Systems with Applications, 91:159–169.
Cirqueira, D., Pinheiro, M. F., Jacob, A., Lobato, F., and Santana, Á. (2018). A literature review in preprocessing for sentiment analysis for brazilian portuguese social media. In 2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI). IEEE.
Deriu, F. and Iezzi, D. (2020). Text analytics in gender studies. introduction.
Dilai, M. and Levchenko, O. (2018). Discourses surrounding feminism in ukraine: A sentiment analysis of twitter data. In 2018 IEEE 13th International Scientific and Technical Conference on Computer Sciences and Information Technologies (CSIT), volume 2, pages 47–50. IEEE.
Duong, H.-T. and Nguyen-Thi, T.-A. (2021). A review: preprocessing techniques and data augmentation for sentiment analysis. Computational Social Networks, 8(1):1–16.
Fuchs, T. and Schäfer, F. (2019). Normalizing misogyny: hate speech and verbal abuse of female politicians on japanese twitter. In Japan forum, pages 1–27. Taylor & Francis.
Goel, R. and Sharma, R. (2020). Understanding the metoo movement through the lens of the twitter. In International Conference on Social Informatics, pages 67–80. Springer.
Greene, D., O’Callaghan, D., and Cunningham, P. (2014). How many topics? stability analysis for topic models. In Joint European conference on machine learning and knowledge discovery in databases, pages 498–513. Springer.
Hino, A. and Fahey, R. A. (2019). Representing the twittersphere: Archiving a representative sample of twitter data under resource constraints. International journal of information management, 48:175–184.
Leung, L., Miedema, S., Warner, X., Homan, S., and Fulu, E. (2019). Making feminism count: integrating feminist research principles in large-scale quantitative research on violence against women and girls. Gender & Development, 27(3):427–447.
Lima Jr., E. G. S., Sousa, G. N., Jacob Jr., A. F. L., and Lobato, F. M. F. (2020). Ferramentas para análise de mídias sociais: Um levantamento sistemático. In Computer on The Beach 2020, pages 389–396. UNIVALE.
Lobato, F., Pinheiro, M., Jacob, A., Reinhold, O., and Santana, Á. (2016). Social crm: Biggest challenges to make it work in the real world. In International Conference on Business Information Systems, pages 221–232. Springer.
Locke, A., Lawthom, R., and Lyons, A. (2018). Social media platforms as complex and contradictory spaces for feminisms: Visibility, opportunity, power, resistance and activism.
Lommel, L. S., Schreier, M., and Fruchtmann, J. (2019). We strike, therefore we are? a twitter analysis of feminist identity in the context of daywithoutawoman. In Forum Qualitative Sozialforschung/Forum: Qualitative Social Research, volume 20:2. DEU.
Medhat, W., Hassan, A., and Korashy, H. (2014). Sentiment analysis algorithms and applications: A survey. Ain Shams engineering journal, 5(4):1093–1113.
Merriam-Webster (2017). Merriam-webster’s 2017 words of the year: Feminism.
Mundt, M., Ross, K., and Burnett, C. M. (2018). Scaling social movements through social media: The case of black lives matter. Social Media+ Society, 4(4).
Nugroho, R., Paris, C., Nepal, S., Yang, J., and Zhao, W. (2020). A survey of recent methods on deriving topics from twitter: algorithm to evaluation. Knowledge and Information Systems, 62(7):2485–2519.
Pereira, D. A. (2021). A survey of sentiment analysis in the portuguese language. Artificial Intelligence Review, 54(2):1087–1115.
Piatetsky, G. (2019). Python leads the 11 top data science, machine learning platforms: Trends and analysis.
Puente, S. N., Maceiras, S. D., and Romero, D. F. (2021). Twitter activism and ethical witnessing: Possibilities and challenges of feminist politics against gender-based violence. Social Science Computer Review, 39(2):295–311.
Reyes-Menendez, A., Saura, J. R., and Filipe, F. (2020). Marketing challenges in the metoo era: Gaining business insights using an exploratory sentiment analysis. Heliyon.
Rodrigues, L., da Silva Junior, J., and Lobato, F. (2019). A culpa é dela! ´E isso o que dizem nos comentários das notícias sobre a tentativa de feminicídio de elaine caparroz. In Anais do VIII Brazilian Workshop on Social Network Analysis and Mining. SBC.
Rodrigues, L., Prado, A., and Lobato, F. M. F. (2022). Pandemia de covid-19 no brasil: uma análise sobre notícias e comentários de usuários. Culturas Midiáticas, 16:26.
Rodriguez, S., Allende-Cid, H., Gonzalez, C., Alfaro, R., Elortegui, C., Palma, W., and Santander, P. (2020). Analyzing lastesis feminist movement in twitter using topic models. In International Conference on Human-Computer Interaction. Springer.
Russell, M. A. (2013). Mining the social web: data mining Facebook, Twitter, LinkedIn, Google+, GitHub, and more. ”O’Reilly Media, Inc.”.
Scarborough, W. J. (2018). Feminist twitter and gender attitudes: Opportunities and limitations to using twitter in the study of public opinion. Socius, 4:2378023118780760.
Scarborough, W. J. and Helmuth, A. S. (2021). How cultural environments shape online sentiment toward social movements: Place character and support for feminism. In Sociological Forum. Wiley Online Library.
Schröer, C., Kruse, F., and Gómez, J. M. (2021). A systematic literature review on applying crisp-dm process model. Procedia Computer Science, 181:526–534.
Stauffer, K. E. and O’Brien, D. Z. (2018). Quantitative methods and feminist political science. In Oxford Research Encyclopedia of Politics. Oxford University Press.
Tajudeen, F. P., Jaafar, N. I., and Ainin, S. (2018). Understanding the impact of social media usage among organizations. Information & Management, 55(3):308–321.
We Are Social (2021). Digital 2021 global overview report.
Yagui, M., Maia, L. F., Ugulino, W., Vivacqua, A., and Oliveira, J. (2017). “bela, recatada e do lar”: Base de dados e aspectos do movimento social ocorrido na rede social online twitter. In Anais do XIV Simpósio Brasileiro de Sistemas Colaborativos. SBC.
Araújo, M. L. D., Diniz, J. P., Bastos, L., Soares, E., Júnior, M., Ferreira, M., Ribeiro, F., and Benevenuto, F. (2016). ifeel 2.0: A multilingual benchmarking system for sentence-level sentiment analysis. In Tenth International AAAI Conference on Web and Social Media.
Belford, M., Mac Namee, B., and Greene, D. (2018). Stability of topic modeling via matrix factorization. Expert Systems with Applications, 91:159–169.
Cirqueira, D., Pinheiro, M. F., Jacob, A., Lobato, F., and Santana, Á. (2018). A literature review in preprocessing for sentiment analysis for brazilian portuguese social media. In 2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI). IEEE.
Deriu, F. and Iezzi, D. (2020). Text analytics in gender studies. introduction.
Dilai, M. and Levchenko, O. (2018). Discourses surrounding feminism in ukraine: A sentiment analysis of twitter data. In 2018 IEEE 13th International Scientific and Technical Conference on Computer Sciences and Information Technologies (CSIT), volume 2, pages 47–50. IEEE.
Duong, H.-T. and Nguyen-Thi, T.-A. (2021). A review: preprocessing techniques and data augmentation for sentiment analysis. Computational Social Networks, 8(1):1–16.
Fuchs, T. and Schäfer, F. (2019). Normalizing misogyny: hate speech and verbal abuse of female politicians on japanese twitter. In Japan forum, pages 1–27. Taylor & Francis.
Goel, R. and Sharma, R. (2020). Understanding the metoo movement through the lens of the twitter. In International Conference on Social Informatics, pages 67–80. Springer.
Greene, D., O’Callaghan, D., and Cunningham, P. (2014). How many topics? stability analysis for topic models. In Joint European conference on machine learning and knowledge discovery in databases, pages 498–513. Springer.
Hino, A. and Fahey, R. A. (2019). Representing the twittersphere: Archiving a representative sample of twitter data under resource constraints. International journal of information management, 48:175–184.
Leung, L., Miedema, S., Warner, X., Homan, S., and Fulu, E. (2019). Making feminism count: integrating feminist research principles in large-scale quantitative research on violence against women and girls. Gender & Development, 27(3):427–447.
Lima Jr., E. G. S., Sousa, G. N., Jacob Jr., A. F. L., and Lobato, F. M. F. (2020). Ferramentas para análise de mídias sociais: Um levantamento sistemático. In Computer on The Beach 2020, pages 389–396. UNIVALE.
Lobato, F., Pinheiro, M., Jacob, A., Reinhold, O., and Santana, Á. (2016). Social crm: Biggest challenges to make it work in the real world. In International Conference on Business Information Systems, pages 221–232. Springer.
Locke, A., Lawthom, R., and Lyons, A. (2018). Social media platforms as complex and contradictory spaces for feminisms: Visibility, opportunity, power, resistance and activism.
Lommel, L. S., Schreier, M., and Fruchtmann, J. (2019). We strike, therefore we are? a twitter analysis of feminist identity in the context of daywithoutawoman. In Forum Qualitative Sozialforschung/Forum: Qualitative Social Research, volume 20:2. DEU.
Medhat, W., Hassan, A., and Korashy, H. (2014). Sentiment analysis algorithms and applications: A survey. Ain Shams engineering journal, 5(4):1093–1113.
Merriam-Webster (2017). Merriam-webster’s 2017 words of the year: Feminism.
Mundt, M., Ross, K., and Burnett, C. M. (2018). Scaling social movements through social media: The case of black lives matter. Social Media+ Society, 4(4).
Nugroho, R., Paris, C., Nepal, S., Yang, J., and Zhao, W. (2020). A survey of recent methods on deriving topics from twitter: algorithm to evaluation. Knowledge and Information Systems, 62(7):2485–2519.
Pereira, D. A. (2021). A survey of sentiment analysis in the portuguese language. Artificial Intelligence Review, 54(2):1087–1115.
Piatetsky, G. (2019). Python leads the 11 top data science, machine learning platforms: Trends and analysis.
Puente, S. N., Maceiras, S. D., and Romero, D. F. (2021). Twitter activism and ethical witnessing: Possibilities and challenges of feminist politics against gender-based violence. Social Science Computer Review, 39(2):295–311.
Reyes-Menendez, A., Saura, J. R., and Filipe, F. (2020). Marketing challenges in the metoo era: Gaining business insights using an exploratory sentiment analysis. Heliyon.
Rodrigues, L., da Silva Junior, J., and Lobato, F. (2019). A culpa é dela! ´E isso o que dizem nos comentários das notícias sobre a tentativa de feminicídio de elaine caparroz. In Anais do VIII Brazilian Workshop on Social Network Analysis and Mining. SBC.
Rodrigues, L., Prado, A., and Lobato, F. M. F. (2022). Pandemia de covid-19 no brasil: uma análise sobre notícias e comentários de usuários. Culturas Midiáticas, 16:26.
Rodriguez, S., Allende-Cid, H., Gonzalez, C., Alfaro, R., Elortegui, C., Palma, W., and Santander, P. (2020). Analyzing lastesis feminist movement in twitter using topic models. In International Conference on Human-Computer Interaction. Springer.
Russell, M. A. (2013). Mining the social web: data mining Facebook, Twitter, LinkedIn, Google+, GitHub, and more. ”O’Reilly Media, Inc.”.
Scarborough, W. J. (2018). Feminist twitter and gender attitudes: Opportunities and limitations to using twitter in the study of public opinion. Socius, 4:2378023118780760.
Scarborough, W. J. and Helmuth, A. S. (2021). How cultural environments shape online sentiment toward social movements: Place character and support for feminism. In Sociological Forum. Wiley Online Library.
Schröer, C., Kruse, F., and Gómez, J. M. (2021). A systematic literature review on applying crisp-dm process model. Procedia Computer Science, 181:526–534.
Stauffer, K. E. and O’Brien, D. Z. (2018). Quantitative methods and feminist political science. In Oxford Research Encyclopedia of Politics. Oxford University Press.
Tajudeen, F. P., Jaafar, N. I., and Ainin, S. (2018). Understanding the impact of social media usage among organizations. Information & Management, 55(3):308–321.
We Are Social (2021). Digital 2021 global overview report.
Yagui, M., Maia, L. F., Ugulino, W., Vivacqua, A., and Oliveira, J. (2017). “bela, recatada e do lar”: Base de dados e aspectos do movimento social ocorrido na rede social online twitter. In Anais do XIV Simpósio Brasileiro de Sistemas Colaborativos. SBC.
Publicado
31/07/2022
Como Citar
COSTA, Geandreson de S.; COUTO, Danielle C. C.; JACOB JUNIOR, Antonio F. L.; LOBATO, Fábio M. F..
Feminismo e Redes Sociais Online: uma Análise de Tweets sobre o Dia Internacional da Mulher. In: BRAZILIAN WORKSHOP ON SOCIAL NETWORK ANALYSIS AND MINING (BRASNAM), 11. , 2022, Niterói.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2022
.
p. 169-180.
ISSN 2595-6094.
DOI: https://doi.org/10.5753/brasnam.2022.223334.
