Exploring Public Reaction To Air Quality On Social Media: The 2022 Saharan Dust Episode In Lisbon

This study analyzes the relationship between air quality and sentiments ex-pressed on social media, focusing on the Saharan dust episode that affected Lisbon in March 2022. Environmental data were collected alongside geolo-cated Twitter posts (now rebranded as X) from the Lisbon area. The content of these posts was analyzed using the Valence Aware Dictionary and sEnti-ment Reasoner algorithm to identify emotional patterns and assess correla-tions with fluctuations in atmospheric pollutants. A predictive analysis was also conducted using Decision Tree and Random Forest machine learning al-gorithms. The findings suggest that despite high levels of inhalable particu-late matter (PM10) during the event, there was no significant change in online sentiments, which remained mostly neutral or slightly positive. This implies that the public's environ-mental perception may not be clearly re-flected on social media, possibly due to cultural factors or the limited influ-ence of Twitter in Portugal. The study concludes that integrating environmen-tal and social data is feasible and valuable, but further research is needed to explore longer periods, other cities, and more advanced techniques, thus contributing to the understanding of air pollution and public perception in Portugal.