## 2 Materials and Methods
### The Case Study  
The model has been tested on an urban area in the city of Naples (Italy); it is a complex urban settlement with a diversity of urban forms that include historic centers with high building and population density, predominantly wooded areas, and mainly industrial areas. In a study carried out in [15], the model proposed in [14] was applied to the city of Naples for the assessment of the impacts generated by heatwaves, taking into account three types of exposure: population density, density of weak groups, and density of residents in fuel poverty. The results of the study highlighted that the major critical issues are obtained by taking into consideration as exposure the density of inhabitants in conditions of fuel poverty. To test our model, we used this exposure, defined as the density of inhabitants who are in families with more than five members or who are unemployed or non-income earners. Following [15], in the test, the area of study was partitioned into census zones in order to have the official population census data, which are released by census section. Subsequently, these data were aggregated by district in order to determine exposure for each of the thirty districts into which the city is divided, where the districts constitute the subzones in which exposure is calculated. In Figure 3, a map representing the districts of Naples is shown. The study area of the city of Naples (Italy) is divided into 30 subzones, represented by districts. As it can be seen from Figure 3, the most densely populated districts are close to the city center, including, in particular, San Lorenzo and Montecalvario, which record the highest population density. In the northern area, the districts of Scampia and Secondigliano  
Figure 3: Thematic map of the districts of Naples classified by population density. record a fair population density, while the peripheral districts located both to the east and to the west record low levels of density compared to all of the others. The tests were performed by selecting a heatwave period in July 2021. To calculate the fuel poverty index, we used the 2021 population census dataset provided by ISTAT, the Italian Institute of Statistics. Subsequently, the data were aggregated by district, and, for each district, the population density in a state of fuel poverty was calculated. It is given by the population in the district of large families with more than 5 members or unemployed or non-income earners, subsequently divided by the extension of the district in km\({}^{2}\)[14]. The social data flow was collected by making queries on the social network X, considering the heatwave period analyzed as the time interval for posting and inserting the name of the district as a hashtag and as keywords terms connected to the heatwave phenomenon and the COVID-19 pandemic. Approximately 17,900 posts were selected, with an average of 600 posts for each district. One-twentieth of these posts were subsequently discarded following the text-parsing process because they were not relevant or pertinent as they did not contain emotional terms or terms associated with the heatwave and/or the COVID-19 pandemic. In order to evaluate the effect of the COVID-19 pandemic as a stressor on impacts/risks, the framework in [15] was used to determine the impact values for each district, considering both fuel poverty, calculated from the census data as exposure, and new fuel poverty, corrected by evaluating the discomfort index of the inhabitants in the district as exposure. The difference between the impact values in the two scenarios measures the influence of the presence of the COVID-19 pandemic on the assessment of the impact/risk scenarios. The model was implemented on a GIS platform built using the ESRI ArcGIS Pro Suite 3.4 and the ESRI ArcPy Python 3.4 libraries. The pleasant and unpleasant emotional categories were identified using the Plutchik wheel of emotions' sixteen primary and secondary emotional categories [23].