The impacts of political activity on fires and deforestation in the Brazilian Amazon rainforest : an analysis of social media and satellite data

Picanço Rodrigues, Vinicius and Caetano, Marco Antonio Leonel (2023) The impacts of political activity on fires and deforestation in the Brazilian Amazon rainforest : an analysis of social media and satellite data. Heliyon, 9 (12). e22670. ISSN 2405-8440 (https://doi.org/10.1016/j.heliyon.2023.e22670)

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Abstract

Social media has become a one-stop shop for consuming news and expressing political views. Politics has become increasingly emotional, and the ensuing polarization has created echo chambers that favor narratives and stories that repeat only one point of view. In this article, we investigated the role of political activity through Twitter (now ‘X’) engagement as a predictor of destructive fires and deforestation in the Brazilian Legal Amazon (BLA). We used a machine learning approach based on sentiment analysis and satellite data. To test the consistency of the sentiment analysis, we compared the timing of messages related to fire and deforestation events with daily fire data from satellites. When comparing positive and negative comments about fires in the BLA, the results showed that the best model for predicting fire outbreaks is the decision tree regressor. We found evidence that positive comments about agriculture, industry, and the Amazon rainforest in response to speeches and statements by high-ranking Brazilian politicians tend to induce positive comments about fire outbreaks and deforestation. These comments then become good predictors of fire outbreaks with a 6-day lag. These results support the view that high-ranking politicians have enormous power to influence damaging events that can have severe impacts on communities, the environment, and the economy. Brazil has seen an unprecedented increase in deforestation and fires in the Amazon rainforest in recent years. Our findings contribute to the growing literature on the role of social media in real-world events and how machine learning approaches can be used to address this class of problems.

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