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.
ORCID iDs
Picanço Rodrigues, Vinicius ORCID: https://orcid.org/0000-0002-6647-9291 and Caetano, Marco Antonio Leonel;-
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Item type: Article ID code: 87547 Dates: DateEvent1 December 2023Published22 November 2023Published Online16 November 2023Accepted22 August 2023SubmittedSubjects: Geography. Anthropology. Recreation > Environmental Sciences Department: Faculty of Engineering > Design, Manufacture and Engineering Management Depositing user: Pure Administrator Date deposited: 08 Dec 2023 11:47 Last modified: 11 Nov 2024 14:09 URI: https://strathprints.strath.ac.uk/id/eprint/87547