Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
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.