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Digital platforms, including social media systems and messaging applications, have become a place for campaigns of misinformation that affect the credibility of the entire news ecosystem. The emergence of fake news in these environments has quickly evolved into a worldwide phenomenon, where the lack of scalable fact-checking strategies is especially worrisome. In this context, this thesis aim at investigating practical approaches for the automatic detection of fake news disseminated in digital platforms. Particularly, we explore new datasets and features for fake news detection to assess the prediction performance of current supervised machine learning approaches. We also propose an unbiased framework for quantifying the informativeness of features for fake news detection, and present an explanation of factors contributing to model decisions considering data from different scenarios. Finally, we propose and implement a new mechanism that accounts for the potential occurrence of fake news within the data, significantly reducing the number of content pieces journalists and fact-checkers have to go through before finding a fake story.

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1	Julio C. S. Reis(julio.reis@dcc.ufmg.br)
2	Fabrício Benevenuto(fabricio@dcc.ufmg.br)

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1	Arun, C. (2019). On whatsapp, rumours, and lynchings.Economic & Political Weekly,54(6):30–35.
2	Dai, E., Sun, Y., and Wang, S. (2020). Ginger cannot cure cancer: Battling fake healthnews with a comprehensive data repository. In Proc. of the Int’l AAAI Conference on Weblogs and Social Media (ICWSM), pages 853–862.
3	Ferrara, E. (2020). What types of covid-19 conspiracies are populated by twitter bots? First Monday, 25(6).
4	Lazer, D. M., Baum, M. A., Benkler, Y., Berinsky, A. J., Greenhill, K. M., Menczer, F.,et al. (2018). The science of fake news. Science, 359(6380):1094–1096.
5	Reis, J., Miranda, M., Bastos, L., Prates, R., and Benevenuto, F. (2016). Uma análise do impacto do anonimato em comentários de notícias online. In Proc. of the Brazilian Symposium on Collaborative Systems (SBSC), pages 46–60.
6	Reis, J. C., Correia, A., Murai, F., Veloso, A., and Benevenuto, F. (2019). Explainable machine learning for fake news detection. In Proc. of the Int’l ACM Conference on Web Science (WebSci), pages 17–26.
7	Reis, J. C., Correia, A., Murai, F., Veloso, A., and Benevenuto, F. (2019). Supervised learning for fake news detection. IEEE Intelligent Systems, 34(2):76–81.
8	Reis, J. C., Kwak, H., An, J., Messias, J., and Benevenuto, F. (2017). Demographics of news sharing in the us twittersphere. In Proc. of the ACM Conference on Hypertext and Social Media (HYPERTEXT), pages 195–204.
9	Reis, J. C., Melo, P., Garimella, K., Almeida, J. M., Eckles, D., and Benevenuto, F.(2020). A dataset of fact-checked images shared on whatsapp during the brazilian and indian elections. In Proc. of the Int’l AAAI Conference on Weblogs and Social Media (ICWSM), pages 903–908.
10	Reis, J. C., Melo, P., Garimella, K., and Benevenuto, F. (2020b). Can whatsapp benefit from debunked fact-checked stories to reduce misinformation? Harvard Kennedy School Misinformation Review.
11	Ribeiro, F., Henrique, L., Benevenuto, F., Chakraborty, A., Kulshrestha, J., Babaei, M.,and Gummadi, K. P. (2018). Media bias monitor: Quantifying biases of social media news outlets at large-scale. In Proc. of the Int’l AAAI Conference on Weblogs and Social Media (ICWSM), pages 290–299.
12	Shu, K., Sliva, A., Wang, S., Tang, J., and Liu, H. (2017). Fake news detection on social media : A data mining perspective. ACM SIGKDD Explorat. Newsletter, 19(1):22–36.

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