RT Journal Article
T1 Hot in Twitter: assessing the emotional impacts of wildfires with sentiment analysis
A1 Loureiro García, María Luz
A1 Alló Pazos, María
A1 Coello Pulido, Pablo
K1 Environmental impacts
K1 Natural language processing
K1 Happiness
K1 Hedonometer
K1 Wildfires
AB Social media generates a significant amount of information in terms of perceptions, emotions, and sentiments. We present an economic analysis using the information provided by Twitter messages, describing impressions and reactions to wildfires occurring in Spain and Portugal. We use natural language processing techniques to analyze this text information. We generate a hedonometer estimate on how sentiments about wildfires vary with exposure, measured via Euclidean distance from the catastrophic event, and air quality. We find that direct exposure to wildfires significantly decreases the expressed sentiment score and increases the expressions of fear and political discontent (protest). Economic valuation of these losses has been computed to be between 1.49€–3.50€/year/Kilometer of distance to the closest active fire. Welfare losses in terms of air quality have been computed as 4.43€–6.59€/day of exposure
PB Elsevier
YR 2022
FD 2022
LK http://hdl.handle.net/10347/29108
UL http://hdl.handle.net/10347/29108
LA eng
NO Ecological Economics 200 (2022) 107502
NO M. Loureiro acknowledges funding from Agencia Estatal de Investigación, RETOS program, grant number PID2019-111255RB-I00
DS Minerva
RD 28 abr 2026