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English Information

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Authors
# Name
1 Mariana Salgueiro(marianadsalgueiro@gmail.com)
2 Sergio Lifschitz(sergio@inf.puc-rio.br)

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Reference
# Reference
1 Alashri, S. and Alalola, T. (2020). Functional analysis of the 2020 U.S. elections on twitter and facebook using machine learning. Procs Intl Conf ASONAM, pages 586–589.
2 Angles, R., Prat-Perez, A., Dominguez-Sal, D., and Larriba-Pey, J.-L. (2013). Benchmarking database systems for social network applications. ACM.
3 Barbosa, C., Felix, L., Alves, A., Xavier, C., and Vieira, V. (2022). Uso de URLs para caracterização de comunidades em redes sociais online. In BRASNAM, pages 25–36
4 BARDlN, L. (1977). Analise de conteúdo. Lisboa: edições , 70:225
5 Bodrunova, S. S., Litvinenko, A. A., and Blekanov, I. S. (2017). Comparing influencers: Activity vs. connectivity measures in defining key actors in twitter ad hoc discussions on migrants in germany and russia. In Intl Conf on Social Informatics, pages 360–376
6 Carley, K. (1991). A theory of group stability. American Soc. Review, 56(3):331–354
7 Costa, L., Reis, A., Bacha, C., Oliveira, G., Silva, M., Teixeira, M., Brandao, M., Lacerda, A., and Pappa, G. (2022). Alertas de fraude em licitações: Uma abordagem baseada em redes sociais. In BRASNAM, pages 37–48.
8 Elbaghazaoui, B. E., Amnai, M., and Fakhri, Y. (2022). Data profiling and machine learning to identify influencers from social media platforms. ICT Stds, pages 201–218
9 Hagen, L., Fox, A., O’Leary, H., Dyson, D., Walker, K., Lengacher, C. A., and Hernandez, R. (2022). The role of influential actors in fostering the polarized covid-19 vaccine discourse on twitter: Mixed methods of machine learning and inductive coding. JMIR Infodemiology, 2(1):e34231
10 Himelboim, I. (2017). Social Network Analysis (Social Media). Wiley
11 Ituassu, A., Lifschitz, S., Capone, L., Vaz, M. B., and Mannheimer, V. (2018). Publicacion de medios y preferencia electoral en twitter: analisis de opinion publica durante las elecciones del ano 2014 en brasil. Palabra Clave, 21(3):860–884.
12 Orabi, M., Mouheb, D., Al Aghbari, Z., and Kamel, I. (2020). Detection of bots in social media: A systematic review. Information Processing & Management, 57(4):102250
13 Paes, V., Araujo, D., Brito, K., and Andrade, E. (2022). Análise de sentimento em tweets relacionados ao desmatamento da floresta amazonica. In BRASNAM, pages 61–72
14 Paul, I., Khattar, A., Kumaraguru, P., Gupta, M., and Chopra, S. (2019). Elites tweet? characterizing the twitter verified user network. In 2019 IEEE 35th International Conference on Data Engineering Workshops (ICDEW), pages 278–285
15 Santia, G. C., Mujib, M. I., and Williams, J. R. (2019). Detecting social bots on facebook in an information veracity context. AAAI Conf Web and Social Media, 13(01):463–472
16 Santos, P. and Goya, D. (2022). Detecc¸ao de posicionamento e rotulação automática de usuarios do twitter: estudo sobre o embate científico-político no contexto da CPI da covid-19. In BRASNAM, pages 49–60
17 Silva, W., Adamo Santana, Lobato, F., and Pinheiro, M. (2017). A Methodology for Community Detection in Twitter. Association for Computing Machinery.
18 Tang, J., Chang, Y., and Liu, H. (2014). Mining social media with social theories. ACM SIGKDD Explorations Newsletter, 15:20–29
19 Wycislik, L. and Warchal, L. (2014). A Performance Comparison of Several Common Computation Tasks Used in Social Network Analysis Performed on Graph and Relational Databases, volume 242. Springer Verlag
20 Zhang, J., Zhang, R., Sun, J., Zhang, Y., Zhang, C., Zhang, J., Zhang, R., Sun, J., Zhang, Y., and Zhang, C. (2016). Truetop: A sybil-resilient system for user influence measurement on twitter. IEEE/ACM Trans. Netw., 24(5):2834–2846