## 6 Conclusions  
In this paper, we deal with the subject of measuring the population's concern with respect to an event prolonged in time. We chose COVID-19 as the subject for our study based on the relevance and global impact it has had. Therefore, we present to the community our very own method to build the _degree of Emotional Concern_, explaining our approach at modeling and quantifying the emotional response to the global pandemic based on data gathered from different geotagged locations using Social Media and dimensional models of emotion. To achieve that, we gathered geolocated data from six different locations over the period of eight months, during the course of two years. Presenting the concepts of _Emotional Baseline_, _Emotional Impact_, _Mood Variation_, and the novel definition of _Searching Behavior Index_, we calculate the _degree of Emotional Concern_ for each location as the result of a normalized, weighted aggregation between the _Mood Variation_ around the emotion _concerned_ and the _SB1_. In order to calculate the _Emotional Rating_, necessary to calculate the _Emotional Charge_, _Emotional Baseline_, _etc._, we rely on the dimensional model PAD for emotion classification introduced by [PERSON], mapping the social interactions on the tridimensional space making use of the eXtended Affective Norms for English Words, which gives VAD values to a set of nearly 14,000 words. This allows us to measure the closeness--or distance--of social interaction to a specific mood, such as _tense_ or _excited_, which we perform for the 15 named moods introduced in Table 1.  
Although Social Media is a growing industry, not all the population, in terms of age range, is using it and, therefore, not all opinions are reflected. This is what drove us to include the Searching Behavior as a parameter when calculating the _degree of Emotional Concern_, so those who are not represented in the interactions we collected can still be captured by this concept, which holds a much bigger audience. There is solid proof in the field supporting the use of Google Trends as a tool to gain insights into the population's interest [53], behavior with respect to an event [38] and even emotional outcome derived from the online search [54]. This past research helps us substantiate the combination with the searching index we developed with the mood variation, in order to create our DEC, which is the novelty we present in this work. Once the DEC for all locations is obtained, we displayed the results found compared against the pandemic evolution (infection rate, death rate, government measures), in order to observe and comprehend its progress. The DEC in all locations reached its peak when the spread of the virus acquired an exponential trend. From that point on, four cities showed a similar evolution, which is a progressive decrease in the DEC. More detailed insights are provided in Figures 12-16, where named mood variations are displayed. These figures show in a more detailed manner how predominant negative emotions arise as a reaction to countermeasures such as lockdown and social restrictions, while they start fading away, being replaced by more neutral and even strong positive emotions once these restrictions are lifted, regardless of the infection rate. Our results shed some light into the field of human behavior and psychology, by bringing to the community a long-term response to a tragic event. We find that populations across countries react in quite similar ways after being exposed to similar restrictions. The work we present here can be used in many scenarios: as we suggested, the notable decrease in people's preoccupation could be linked to human resilience or adaptation to prolonged situations, even if they are catastrophic. This research could be continued by exploring other locations and for longer periods of time: we used as a baseline the previous year, thus extending our range to the previous years might give us a more rigorous measure.