## 5 Experiment Results and Discussion
This concurs with findings from other authors, where social media monitoring revealed an increase in emotions such as anger, fear, and anxiety soon after the beginning of the pandemic [46]. Psychology also shows that people tend to feel anxious and unsafe in response to environment changes, especially in the case of disease outbreaks, when the cause or progression of the disease and outcomes are unclear, rumors grow and close-minded attitudes eventuate [47]. In most countries, during the first phase of the pandemic, rates for depression, anxiety, and insomnia increased significantly in comparison to previous years, while these rates noticeably decreased as time passed since the beginning of the pandemic [48; 49; 50]. This observation may lead us to argue that, in general terms, a decrease in DEC means an adaptation or getting accustomed to living in such a tragic situation, where, although there is an increase in the cumulative incidence of COVID-19 cases, the response, if any, is not so negative. This aspect of acquired indifference has been studied numerous times in the field of human psychology, associated with the need for survival and resilience in the face of extremely aversive events [51]. Other authors already demonstrated that we not only become numb to the significance of increasing numbers, but our compassion can actually fade or collapse overall as numbers increase [52]. Similarly, we can see how the emotional response spreads over the individual moods in Figures 12-16, providing us with a more detailed insight on the variation of specific emotions over that period of time. It is important to emphasize the reach, scope, and limitations of our method: we make use of the aggregated opinions of a collective, which we consider representative for a whole location, collected for two periods of time, in order to create a new method to quantify and calculate an index that can measure the emotional concern of the population. The first assumption we make is choosing the selected target group as a representative for the whole population. By choosing a specific timeframe, we are aware of possible mood changes that we do not capture, and this could directly influence the results we present. Our Social Media analysis assumes that the total population studied in this work expresses their complete and absolute opinion via these networks, thus, even contemplating the possibility that people use Twitter to communicate only certain emotions (i.e., angry tweets, hate speech, etc. ), we dismiss this case, as the scientific community provides sufficient support to utilize this practice [11; 12; 19; 34; 46; 40]. We establish our criteria to obtain good candidates in all transparency, aiming to build an unbiased dataset. Secondly, we build our method based on a lexicon. We decided to choose this approach instead of more recent architectures based on the format of the data we work with: opinions expressed in a few words or sentences. Although current state-of-the-art architectures provide exceptional results in text classification, as classes increase, their performance decrease. Adding to this, our resources did not allow for a labeled dataset upon which to train such models. Accepting the limitations of a lexicon, it provides valid representations of its aggregated values on PA(D) models, as shown by previous works from other authors [34; 35; 46]. On top of that, emotion labeling and classification still is a fuzzy field in psychology, existing no consensus over a global emotion model, thus choosing a specific approach bears the exclusion of different methods. Nevertheless, being aware of the possible limitations of our method, we establish that the scope of this study is to build the degree of Emotional Concern based on two relevant factors: Emotional Impact and Searching Behavior.