Abstract
This work presents an analysis of Brazilian political discourse from speeches and social media posts, focusing on the ability to transfer learned models’ knowledge between different contexts. The analysis is conducted through PoliS, a new resource containing two datasets of political discussions labeled for party and ideological leaning from congressional speeches and social media posts by political influencers. The transfer learning experiments are performed using the transcripts of the congressional speeches to train a model used to predict the political leaning of social media influencers. To evaluate the robustness of the model, the analysis includes a time-lag study of the performance degradation of the transferred model. We find that relatively little social media data (about one hour) is needed to achieve reasonable performance in classification, and that performance does not degrade significantly over time.
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Acknowledgment
This work was supported by Portuguese national funds through Fundação para a Ciência e a Tecnologia (FCT) under references UIDB/50021/2020 and SFRH/BD/145561/2019.
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Caled, D., Silva, M.J. (2022). A Transfer Learning Analysis of Political Leaning Classification in Cross-domain Content. In: Pinheiro, V., et al. Computational Processing of the Portuguese Language. PROPOR 2022. Lecture Notes in Computer Science(), vol 13208. Springer, Cham. https://doi.org/10.1007/978-3-030-98305-5_25
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