Large-Scale And Long-Term Characterization Of Political Communications On Social Media

  • Lucas Santos de Oliveira UESB
  • Pedro Olmo Stancioli Vaz de Melo UFMG


Social media play an important role in shaping political discourse, creating a public sphere that enables discussions, debates, and deliberations. Aware of this importance, politicians use social media for self-promotion and as a means of influencing people and votes. As an example of this assertion, in 2018, Brazilians democratically elected for president the far-right candidate Jair Bolsonaro. One of the most surprising feats of this outcome is that his party, PSL, had almost no television time. His victory was only possible because of his supporters’ engagement and activism on social media platforms, such as Twitter, Facebook, and WhatsApp. In this context, politicians need to decide how to communicate with their voters to build their reputations. While some politicians only share professional communications about their political agenda and activities, others prefer a more non-political and informal approach, sharing communications about the most varied subjects, such as religion, sports, and their families. Others, however, misuse platforms by spreading political messages that violate policies and circumvent electoral laws. Aware of these problems, I propose the LOCPOC a methodology to characterize the communication of Brazilian politicians over years in terms of the amount of political and non-political messages they post. The methodology is robust to concept drifts over time, requiring few new labeled messages each year. From the classified messages, I was able to characterize the communication of politicians over time and identified new findings: (i) Brazilian congresspeople changed their communication behavior over time; (ii) concept drifts occurred during important events in Brazilian politics; (iii) the explosive rise of the right seen just before the 2018 elections; (iv) a broader and more evenly distributed right-wing participation than the left-wing, and, finally, (v) the increase of public engagement over time.

Palavras-chave: political, social media, communication, characterization


Marcelo Santos Amaral and José Antônio Gomes de Pinho. 2016. Tuitando por Votos: Congressistas Brasileiros e o Uso do Twitter nas Eleições de 2014. In Proceedings of the XL Encontro da Anpad. Anpad,1–19.

Josemar Alves Caetano, Jussara Almeida, and Humberto T. Marques Neto. 2018. Characterizing politically engaged users’ behavior during the 2016 us presidential campaign. Proceedings of the 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2018 (2018), 523–530.

Albert Franca Josua Costa, Regis Antonio Saraiva Albuquerque, and Eulanda Miranda Dos Santos. 2018. A Drift Detection Method Based on Active Learning. Proceedings of the International Joint Conference on Neural Networks 2018-July (2018).

Council on Foreign Relations. 2018. WhatsApp’s Influence in the Brazilian Election and How It Helped Jair Bolsonaro Win. [link].

Lauren Feiner. 2019. Twitter Bans poltical ads – CNBC. [link].

Lei Gao, Alexis Kuppersmith, and Ruihong Huang. 2017. Recognizing Explicit and Implicit Hate Speech Using a Weakly Supervised Two-path Bootstrapping Approach. arXiv:1710.07394

Will J. Grant, Brenda Moon, and Janie Busby Grant. 2010. Digital dialogue? australian politicians’ use of the social network tool twitter. Australian Journal of Political Science 45, 4 (2010), 579–604.

Didier Grimaldi. 2019. Can we analyse political discourse using Twitter? Evidence from Spanish 2019 presidential election. Social Network Analysis and Mining 9, 1 (2019), 49.

Yu He, Jianxin Li, Yangqiu Song, Mutian He, and Hao Peng. 2018. Time-evolving text classification with deep neural networks. IJCAI International Joint Conference on Artificial Intelligence 2018-July (2018), 2241–2247. [link]

Andreas Jungherr. 2016. Twitter use in election campaigns: A systematic literature review. Journal of Information Technology & Politics 13,1 (jan 2016), 72–91.

Bartosz Krawczyk, Leandro L. Minku, João Gama, Jerzy Stefanowski,and Michał Woźniak. 2017. Ensemble learning for data stream analysis: A survey. Information Fusion 37 (2017), 132–156.

Caio Machado, Beatriz Kira, Gustavo Hirsch, Nahema Marchal, Bence Kollanyi, Philip N Howard, Thomas Lederer, and Vlad Barash. 2018. News and political information consumption in Brazil: Mapping the first round of the 2018 Brazilian presidential election on Twitter. Computational Propaganda Project (2018).

Lucas S. Oliveira, Pedro Vaz de Melo, Marcelo Amaral, and José Antônio Pinho. 2018. When Politicians Talk About Politics: Identifying Political Tweets of Brazilian Congressmen. International AAAI Conference on Web and Social Media (2018).

Lucas Santos de Oliveira, Marcelo Santos Amaral, and Pedro OS Vaz de Melo. 2021. Long-term Characterization of Political Communications on Social Media. In IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology. 95–102.

Lucas Santos de Oliveira, Pedro O. S. Vaz-de Melo, Marcelo S. Amaral, and José Antônio G. Pinho. 2020. Do Politicians Talk about Politics? Assessing Online Communication Patterns of Brazilian Politicians. ACM Transactions on Social Computing 3, 4, Article 19 (Sept. 2020), 28 pages.

Lucas Santos de Oliveira and Pedro O.S. Vaz Melo. 2017. How to Find the Relevant Words Politicians Use in Twitter?. In Proceedings of the 23rd Brazillian Symposium on Multimedia and the Web - WebMedia ’17. ACM Press, New York, New York, USA, 465–468.

Joyojeet Pal. 2015. Banalities turned viral: Narendra modi and the political tweet. Television and New Media 16, 4 (2015), 378–387.

Raquel Recuero, Felipe Bonow Soares, and Anatoliy Gruzd. 2020. Hyperpartisanship, Disinformation and Political Conversations on Twitter: The Brazilian Presidential Election of 2018. Ted Rogers School of Management, 3 Social Media Lab 1, 2 (2020), 569–578.

Márcio Silva, Lucas Santos de Oliveira, Athanasios Andreou, Pedro Olmo Vaz de Melo, Oana Goga, and Fabricio Benevenuto. 2020. Facebook Ads Monitor: An Independent Auditing System for Political Ads on Facebook. In Proceedings of The Web Conference 2020. ACM, New York, NY, USA, 224–234.

Andranik Tumasjan, Timm Oliver Sprenger, Philipp G Sandner, and Isabell M Welpe. 2010. Predicting Elections with Twitter: What 140 Characters Reveal about Political Sentiment. ICWSM 10 (2010), 178–185.

Ingmar Weber, Venkata R Kiran Garimella, and Alaa Batayneh. 2013. Secular vs. islamist polarization in egypt on twitter. In Proceedings of the 2013 IEEE/ACM international conference on advances in social networks analysis and mining. 290–297.

Indre Zliobaite, Albert Bifet, Bernhard Pfahringer, and Geoffrey Holmes. 2014. Active learning with drifting streaming data. IEEE Transactions on Neural Networks and Learning Systems 25, 1 (2014), 27–39.
OLIVEIRA, Lucas Santos de; MELO, Pedro Olmo Stancioli Vaz de. Large-Scale And Long-Term Characterization Of Political Communications On Social Media. In: CONCURSO DE TESES E DISSERTAÇÕES - SIMPÓSIO BRASILEIRO DE SISTEMAS MULTIMÍDIA E WEB (WEBMEDIA), 28. , 2022, Curitiba. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2022 . p. 31-34. ISSN 2596-1683. DOI: