Impact Analysis of the Ministry of Women’s News on Calls to the 180 Hotline
Abstract
This study explores the influence of news from the Ministry of Women on the number of calls to the Disque 180 helpline, utilizing a quantitative approach that combines time series techniques with advanced statistical models. We analyzed time series of news, both with and without a specific filter on the theme of violence against women, to assess the direct impact on public reporting behavior. Methods included the Gaussian process, Granger causality, vector autoregressive model, and transfer entropy. The analysis was assertive, emphasizing the importance of the publications and periodic actions by the Ministry of Women.References
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Women, U. (2019). Un women: Ending violence against women. [link]. Acesso em: 21 mar. 2025.
Zaremba, A. and Peters, G. (2020). Statistical causality for multivariate non-linear time series via gaussian processes. Available at SSRN 3609497.
Zivot, E. and Wang, J. (2006). Vector autoregressive models for multivariate time series. Modeling financial time series with S-PLUS®, pages 385–429.
Barnett, L., Barrett, A. B., and Seth, A. K. (2009a). Granger causality and transfer entropy are equivalent for gaussian variables. Physical review letters, 103(23):238701.
Barnett, L., Barrett, A. B., and Seth, A. K. (2009b). Granger causality and transfer entropy are equivalent for gaussian variables. Physical review letters, 103(23):238701.
Barnett, L. and Bossomaier, T. (2012). Transfer entropy as a log-likelihood ratio. Physical review letters, 109(13):138105.
Bossomaier, T., Barnett, L., Harré, M., Lizier, J. T., Bossomaier, T., Barnett, L., Harré, M., and Lizier, J. T. (2016). Transfer entropy. Springer.
Box, G. E., Jenkins, G. M., Reinsel, G. C., and Ljung, G. M. (2015). Time series analysis: forecasting and control. John Wiley & Sons.
Cai, J., Goudie, R. J., Starr, C., and Tom, B. D. (2024). Dynamic factor analysis with dependent gaussian processes for high-dimensional gene expression trajectories. Biometrics, 80(4):ujae131.
Chalkiadakis, I., Zaremba, A., Peters, G. W., and Chantler, M. J. (2022). On-chain analytics for sentiment-driven statistical causality in cryptocurrencies. Blockchain: Research and Applications, 3(2):100063.
ELY, R. A. (2019). Séries de tempo, aula 2, sazonalidade e tendência. [link]. Acesso em: 21 mar. 2025.
Federal, G. (2024). Central de atendimento à mulher – ligue 180. [link]. Acesso em: 21 mar. 2025.
Federal, G. (2025). Últimas notícias. [link]. Acesso em: 14 mai. 2025.
Fonnesbeck, C. (2022). Fitting gaussian process models in python. [link]. Acesso em: 21 mar. 2025.
Fonseca, N., Rivero, M., et al. (2020). Causalidade à granger em ciências sociais: um guia para a investigação aplicada. Egitania Sciencia, 2(27):21–36.
Katy (2021). Performing “granger causality” with python: Detailed examples. [link]. Acesso em: 21 mar. 2025.
Kwak, S. (2023). Are only p-values less than 0.05 significant? a p-value greater than 0.05 is also significant! Journal of Lipid and Atherosclerosis, 12(2):89.
Maria, G. and Bittar, P. (2018). Ligue 180 é o mais importante projeto de enfrentamento à violência contra a mulher, diz secretária. [link]. Acesso em: 21 mar. 2025.
Maucher, D. J. (2022). Gaussian process: Implementation in python. [link]. Acesso em: 21 mar. 2025.
Moore, D. G. (2019). Time series measures. [link]. Acesso em: 21 mar. 2025.
Neha (2024). Developing vector autoregressive model in python! [link]. Acesso em: 21 mar. 2025.
OMS (2021). Oms: uma em cada 3 mulheres em todo o mundo sofre violência. [link]. Acesso em: 21 mar. 2025.
Rosoł, M., Młyńczak, M., and Cybulski, G. (2022). Granger causality test with nonlinear neural-network-based methods: Python package and simulation study. Computer Methods and Programs in Biomedicine, 216:106669.
Tsay, R. S. (2000). Time series and forecasting: Brief history and future research. Journal of the American Statistical Association, 95(450):638–643.
Women, U. (2019). Un women: Ending violence against women. [link]. Acesso em: 21 mar. 2025.
Zaremba, A. and Peters, G. (2020). Statistical causality for multivariate non-linear time series via gaussian processes. Available at SSRN 3609497.
Zivot, E. and Wang, J. (2006). Vector autoregressive models for multivariate time series. Modeling financial time series with S-PLUS®, pages 385–429.
Published
2025-07-20
How to Cite
RIBEIRO, Murilo U. G.; COSTA, Keila B.; FERNANDES, Sheyla C.; AQUINO, Andre L..
Impact Analysis of the Ministry of Women’s News on Calls to the 180 Hotline. In: LATIN AMERICAN SYMPOSIUM ON DIGITAL GOVERNMENT (LASDIGOV), 12. , 2025, Maceió/AL.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 145-156.
ISSN 2763-8723.
DOI: https://doi.org/10.5753/lasdigov.2025.8964.
