*4.2. The Proposed Methodology Applied to the Case of Granada*

The methodology described above was applied, point by point, on the case study of Granada.


We carried out a web investigation on social media that collects the citizens' opinion on this matter. We identified two associations spreading information on the topic through their blogs and social platform (Facebook e Twitter). The first one, "*Bajo Albayzin*", is a neighbourhood association founded in January 2000. It aims to improve the cultural, historical, social, urban, and environmental values of the Albayzin district. The second association, "*SOS Alhambra*", was founded in July 2015 following a citizen's protest against an architectural design involving a major transformation of the "atrium" area of the Alhambra, in front of the Carlos V palace. The aim of the organization is to warn about the possible environmental damage that the project could cause to the site, which is one of the UNESCO World Heritage Sites and it also generally provides information on the main problems affecting the monumental complex.


### *Sustainability* **2020**, *12*, 731


**Figure 3.** Search keywords—screen application from Social Insider.

We decided to focus on "*Bajo Albayzin*" because it hosts more articles regarding the citizens' protests than "*Sos Alhambra*", which appears to prefer political and administrative topics. The "*Bajo Albayzin*" association is in a direct relationship with the residents of the district and their problems, being themselves active in the neighbourhood. Moreover, Figure 4 shows that the engagement trend in "*Bajo Albayzin*" is higher than in "*Sos Alhambra*" in terms of shares, comments, publications, and reactions.

**Figure 4.** See the distribution of total engagement for each page—report Social Insider.

	- saving data for unlimited time;
	- identification of the most performing posts;
	- multi-page comparisons;
	- analysis of own posts and those of competitors;
	- extraction of reports in CSV, PDF, PPT format.

The tool can create charts automatically, providing information about the posts and the distribution of shares and reactions to posts over time.

6. *Analysis of the key metrics*: we analysed the key metrics of posts, fans, fan growth, and engagement from 2015 to date; as we can see (Figure 5) the fan growth remained unchanged, while the page remains active for posts publication.

*Sustainability* **2020**, *12*, 731


**Figure 5.** Key Metrics "Bajo Albayzin"—report from Social Insider.

In particular, owing to the machine learning tool of the Social Insider, we were able to trace the distribution of the various posts—links, photos, videos, events, albums—in the time frame of interest.

The orange histogram bars, compared to the much shorter azure and blue, suggest which type of post is preferred: links are published significantly more often than other multimedia content (Figure 6).

**Figure 6.** Total post by type "*Bajo Albayzin*"—report from Social Insider.

The association, in fact, uses Facebook as a vehicle to spread information. Any new content, already made known by blogs or newspaper articles, is re-shared on the page to increase its circulation rate.

7. Collection *of reactions to selected posts.* The analysis of reactions was carried out through Facebook *Reactions* (Figure 7).

**Figure 7.** Reactions from Facebook.

Social Insider automatically generates a graph that expresses the reactions trend excluding *like* reactions. Figure 8 shows that the peaks of the function "*angry*" (in blue) increase over the years. Instead, the others (ahah, sad, love, wow) remain stable over time. The "*angry*" reaction curve refers to several topics and not only to those related to tourism protests.

**Figure 8.** "*Angry*" reaction curve trend on "Bajo Albayzin" page.


**Figure 9.** Comparison between *A* = *f*(*x*) and *B* = *z*(*x*).

c. In order to compute the dissatisfaction index, we calculated the integrals of both functions *A* = *f*(*x*) and *B* = *z*(*x*). For example, the integral referred to in 2016 is:

$$TA\_d = \int\_{2016}^{2017} A \, dx \tag{4}$$

<sup>8.</sup> *Dissatisfaction* Index *Calculation*:

$$TA\_b = \int\_{2016}^{2017} B \, d\mathbf{x} \tag{5}$$

d. Figure 10 shows the areas under the A curve for each year. The "*angry*" reactions have a peak in conjunction with the triggering events. As we can see, the highest peak is in 2017. Instead, in 2019 there is a lower value probably due to the citizens' acquiescence to the flow of tourists in Granada.

**Figure 10.** "*Angry*" reactions trend over time.

e. The "C.1.2 Index modified", for example, for the year 2017, is as follows:

$$I\_{\rm diss} = \frac{TA\_4}{TA\_{aR}} = \frac{\int\_{2016}^{2017} A \, dx}{\int\_{2016}^{2017} B \, dx} \tag{6}$$

The ratio *Idiss* defines the incidence of citizens' dissatisfaction with the flow of tourism in relation to the totality of the topics on the page. The higher percentage is in 2016 (Table 3).


**Table 3.** Comparison among dissatisfaction indexes over four years.

We analysed the posts related to mass tourism for each peak of curve (A) and found that they are related to the following main events:

