**3. Data and results**

A total mass of nearly 35,000 tweets (short texts of maximum 140 characters each) has been retrieved from the Twitter web platform during the survey period. All of them have been subjected to text analysis for individuating the use of Blue Flag program keywords.1 Only recent data have been collected, because older data might be partial due to random deletion, and thus unfeasible. **Table 3** reports their distribution in relation to place-destination and keywords as assumed in the above **Tables 1** and **2**. **Table 4** reports data relating to tweets emitted within a 5 km radius area from the center place.

The adopted research method is qualitative. Yet, the collected data allow some descriptive quantitative analysis. Numbers show some neat pieces of evidence that are worthy of mention.

The first finding to be stressed is that the most used "keywords" found in the collected tweets are the "names" of the singular destinations. This result was expected because tourists "must" obviously cite the name of the singular place when talking about it. Anyway, this data is useful too because it can help in sorting the places along two significative ladders. The first one is the sort

<sup>1</sup> All recorded data are retrievable at the authors' digitalized archive.


**3. Data and results**

1

A total mass of nearly 35,000 tweets (short texts of maximum 140 characters each) has been retrieved from the Twitter web platform during the survey period. All of them have been subjected to text analysis for individuating the use of Blue Flag program keywords.1

**English Italian Croatian**

Pollution Inquinamento Zagađenje Recyclable Riciclabile Reciklirati Safety Sicurezza Sigurnost Sea Mare More

Oil Petrolio Ulje

114 Mobilities, Tourism and Travel Behavior - Contexts and Boundaries

Marine areas Aree marine Morskih područja

Physically disabled Disabile Tjelesnih invalida

Sensitive area Area sensibile Osjetljivom području

Waste bins Cestini della spazzatura Košarica za otpad Waste containers Contenitori per la spazzatura Spremnici za otpad Waste-water Acque inquinate Otpadnih voda

Sewage Fognature Kanalizacija Sustainable development Sviluppo sostenibile Održivi razvoj

Toilet Bagno WC

Water Acqua voda

Water quality Qualità dell'acqua Kakvoće voda

recent data have been collected, because older data might be partial due to random deletion, and thus unfeasible. **Table 3** reports their distribution in relation to place-destination and keywords as assumed in the above **Tables 1** and **2**. **Table 4** reports data relating to tweets

The adopted research method is qualitative. Yet, the collected data allow some descriptive quantitative analysis. Numbers show some neat pieces of evidence that are worthy of mention. The first finding to be stressed is that the most used "keywords" found in the collected tweets are the "names" of the singular destinations. This result was expected because tourists "must" obviously cite the name of the singular place when talking about it. Anyway, this data is useful too because it can help in sorting the places along two significative ladders. The first one is the sort

emitted within a 5 km radius area from the center place.

**Table 2.** Languages and words used (keywords) for text analysis.

All recorded data are retrievable at the authors' digitalized archive.

Only

5Promotionally

 used by tourist

operators.

Analysis of Online Conversations for Giving Sense to Sustainable Tourism in the Adriatic-Ionian Region http://dx.doi.org/10.5772/intechopen.70371 115

**Table 3.** Tweets per place and keywords.


**Table 4.** Tweets emitted in a 5 km radius area from the center place per keywords.

of places per number of citations, indicating the popularity of the singular destination. The second is the sequence of environmental criteria per number of citations and per singular destination. This sorting can indicate the grade of importance attributed to each criterion. Anyway, this sorting of destinations and criteria is strictly dependent on the twittering behavior of tourists.

The most cited destination is Cesenatico, followed by San Benedetto del Tronto, then Split and Makarska. The latter is surprisingly mentioned in fewer tweets than expected notwithstanding it is a very known and reputed Croatian seaside destination. As already said, the search term "split" is affected by white noise due to contingencies, for the English meaning of the word and a homonymous movie aired in the period of the survey. Adding the #split search term, the eventual white noise was put in evidence.

Both the two Italian destinations are more cited than those situated on the Croatian coast. Furthermore, another evidence regards the sorting of the two Italian resorts. Namely, Cesenatico, located in the Emilia-Romagna, does precede San Benedetto del Tronto, situated in the Marche region. This is an expected finding because Emilia-Romagna is much more renowned than the Marche region as a seaside destination.

The quantitative analysis regarding the "environmental criteria" also gives some ex-ante expected results about the most used keywords in twittering. Within the Blue Flag criteria, the keywords "sea" and "beach" are the most used ones in all the considered languages. Nevertheless, it is worth to compare this sorting with the preceding ladders regarding the Italian and Croatian destinations. In fact, when linked to singular destinations, they put the two Italian destinations before the Croatian ones. Data say with sufficient evidence that twittering tourists talk more about the two Italian destinations than the Croatian ones.

The preceding findings derive from the analysis of tweets talking about the singular destination without considering the geographical source of emission. They can come from everywhere, and they have been gathered because they contain a reference to a selected place. As stated in the previous section about the procedure for data achieving, also a geo-referenced constraint has been adopted in order to gather the tweets coming from the close area surrounding the singular resort.

Data exposed in **Table 4** show the total amount and distribution per keywords of tweets emitted from a 5 km radius area from the center place. These data cannot be compared to those in **Table 3** due to different languages used by tweeters. Anyway, they are useful because they can help in understanding if far or close twitters were talking about the four destinations. Then, a sorting using this criterion can be made.

Findings are intriguing because they show only partially a similar sorting dependent on keywords related to sustainable tourism. The two Italian resorts are together more "locally" commented than the Croatian ones. Cesenatico is in the first position, but the second is Split, followed by San Benedetto del Tronto, and Makarska. Anyway, the distance between Split and San Benedetto del Tronto is very thin. Prudently, one could say that the Italian resorts attract local tourists more than the Croatian ones.

It must be stressed that the global amount of tweets talking about the environmental issues of the four destinations are a very residual part. This is valid even for tweets emitted from the close area around each singular destination. The finding is remarkable also because no substantial differences emerged between the two coasts. This evidence is even more empowered by the fact that keywords referring directly to the eco-label Blue Flag are literally zero.

Besides the exposed quantitative analysis of data, a text analysis has been made by directly reading the texts of tweets. The main findings are described in the following.

The narrative about the selected destinations is focused on the leisure time tourists were spending during the vacation period. It is possible to state that all the texts are oriented to describe the leisure aspects of the singular destinations rather than their environment and sustainability features.

As an example of the detected typical behavior of tourists, a screenshot from a private Twitter account is reported in **Figure 1**.

**Figure 1.** Screenshot from Twitter. Private user.

of places per number of citations, indicating the popularity of the singular destination. The second is the sequence of environmental criteria per number of citations and per singular destination. This sorting can indicate the grade of importance attributed to each criterion. Anyway, this sorting of destinations and criteria is strictly dependent on the twittering behavior of tourists.

**Beach Sea Blue Flag Flag Marine** 

Cesenatico 3861 171 326 16 0 14 3 87 44 126

Makarska 292 41 27 4 0 8 2 5 2 2 Split 3344 171 196 49 2 46 0 36 31 123

**area**

3252 98 239 33 5 11 3 40 40 84

**Water quality** **Toilet Ecolabel Other1**

The most cited destination is Cesenatico, followed by San Benedetto del Tronto, then Split and Makarska. The latter is surprisingly mentioned in fewer tweets than expected notwithstanding it is a very known and reputed Croatian seaside destination. As already said, the search term "split" is affected by white noise due to contingencies, for the English meaning of the word and a homonymous movie aired in the period of the survey. Adding the #split

Both the two Italian destinations are more cited than those situated on the Croatian coast. Furthermore, another evidence regards the sorting of the two Italian resorts. Namely, Cesenatico, located in the Emilia-Romagna, does precede San Benedetto del Tronto, situated in the Marche region. This is an expected finding because Emilia-Romagna is much more

The quantitative analysis regarding the "environmental criteria" also gives some ex-ante expected results about the most used keywords in twittering. Within the Blue Flag criteria, the keywords "sea" and "beach" are the most used ones in all the considered languages. Nevertheless, it is worth to compare this sorting with the preceding ladders regarding the Italian and Croatian destinations. In fact, when linked to singular destinations, they put the two Italian destinations before the Croatian ones. Data say with sufficient evidence that twittering tourists talk more about the two Italian destinations than the Croatian ones.

The preceding findings derive from the analysis of tweets talking about the singular destination without considering the geographical source of emission. They can come from everywhere, and they have been gathered because they contain a reference to a selected place. As stated in the previous section about the procedure for data achieving, also a geo-referenced constraint has been adopted in order to gather the tweets coming from the close area sur-

search term, the eventual white noise was put in evidence.

**Table 4.** Tweets emitted in a 5 km radius area from the center place per keywords.

**Center place Total Per keywords From 5 km radius area**

116 Mobilities, Tourism and Travel Behavior - Contexts and Boundaries

San Bendetto del Tronto

To be compared with **Table 2.**

1

renowned than the Marche region as a seaside destination.

rounding the singular resort.

The text says: "Yes, rather than Rio or Ipanema… Cesenatico is much better! @On the Beach in Cesenatico."

The date of the tweet is coincident with the Olympic games in Brazil. The Italian tourist is spending his vacation in Italy, and a comparison between the most famous Brazilian beach and Cesenatico could be quite daring, so the tone is ironic. Anyway, the immediate attention is oriented to leisure time, at least in a satisfactory way and in a domestic destination. No mention of any environmental issue is detectable.

Even the following tweet displays a typical way and mood of texting: "Water of the pool is green because I'm actually in Cesenatico. #Rio2016 #RioOlympics2016."

It helps illustrating the ambiguity of texting and the necessity to interpret the mood and the sense of the sentiment. The green color is usually used for labeling good environmental conditions. In this case, the tone is ironic against the quality of water in Cesenatico that shows the same color of the accidentally polluted swimming pools of Rio de Janeiro Olympic games instead of the usual blue.

Only a few other tweets talking about the water conditions have been detected. They are mainly referred to Italy using the same ironic tone for describing the bad quality of the sea or the general scarce cleaning of places.

**Figure 2.** Screenshot from Twitter. Public user.

A fundamental finding of the research is that no public body used Twitter for supporting its own activity and reputation about tourism and sustainability. They used this web tool in a very sparse mode, often privileging a security vision.

As an example of this behavior, a screenshot from a public Twitter account is reported in **Figure 2**. The twittering body is the Municipal Police of Cesenatico and the text says: "#safebeaches #Cesenatico To buy from #abusive vendors is not convenient: don't do it! And the text in the attached picture is: Do you know why an abusive seller costs less? Because you pay his/her own taxes."
