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The main aim of this paper is to build an approach to analyze the tourist content posted on social media. The approach incorporates information extraction, cleaning, data processing, descriptive and content analysis and can be used on different social media platforms such as Instagram, Facebook, etc. This work proposes an approach to social media analytics in traveler-generated content (TGC), and the authors use Twitter to apply this study and examine data about the city and the province of Granada.

In order to identify what people are talking and posting on social media about places, events, restaurants, hotels, etc. the authors propose the following approach for data collection, cleaning and data analysis. The authors first identify the main keywords for the place of study. A descriptive analysis is subsequently performed, and this includes post metrics with geo-tagged analysis and user metrics, retweets and likes, comments, videos, photos and followers. The text is then cleaned. Finally, content analysis is conducted, and this includes word frequency calculation, sentiment and emotion detection and word clouds. Topic modeling was also performed with latent Dirichlet association (LDA).

The authors used the framework to collect 262,859 tweets about Granada. The most important hashtags are #Alhambra and #SierraNevada, and the most prolific user is @AlhambraCultura. The approach uses a seasonal context, and the posted tweets are divided into two periods (spring–summer and autumn–winter). Word frequency was calculated and again Granada, Alhambra are the most frequent words in both periods in English and Spanish. The topic models show the subjects that are mentioned in both languages, and although there are certain small differences in terms of language and season, the Alhambra, Sierra Nevada and gastronomy stand out as the most important topics.

Extremely difficult to identify sarcasm, posts may be ambiguous, users may use both Spanish and English words in their tweets and tweets may contain spelling mistakes, colloquialisms or even abbreviations. Multilingualism represents also an important limitation since it is not clear how tweets written in different languages should be processed. The size of the data set is also an important factor since the greater the amount of data, the better the results. One of the largest limitations is the small number of geo-tagged tweets as geo-tagging would provide information about the place where the tweet was posted and opinions of it.

This study proposes an interesting way to analyze social media data, bridging tourism and social media literature in the data analysis context and contributes to discover patterns and features of the tourism destination through social media. The approach used provides the prospective traveler with an overview of the most popular places and the major posters for a particular tourist destination. From a business perspective, it informs managers of the most influential users, and the information obtained can be extremely useful for managing their tourism products in that region.

A food tourism destination can fully exploit its competitiveness if food-related attributes are consistently highlighted both in its promotion and in user-generated content. However, in the context of food tourism research, a possible image incongruence has not yet been studied. Tourism destination image incongruence occurs when different travel information sources reflect inconsistent representations of a destination's attributes. This study addresses this gap, focusing on Italian food and wine as drivers to attract visitors. This study examines whether food-related attributes are present in online travel-related conversations and are perceived differently by people with and without knowledge about the destination.

Content analysis based on a Bayesian machine-learning technique utilizing Leximancer software was applied to analyze questions and answers posted on TripAdvisor forums by potential and past visitors of four destinations in Italy (Naples, Florence, Parma and Ferrara). Questions and answers expressed by people with different knowledge in Italian and English were analyzed separately to gain deeper understanding.

Contrary to expectations, food-related themes were almost completely absent in the conversations analyzed, with only a few exceptions in Italian question sections. This situation depicts a sort of “cannibalism”, in the sense that the centrality of food-related attributes is engulfed by other, less sensorial, enjoyable and memorable aspects of the travel experience.

Analysis suggests that hype may exist in food tourism promotion related to destination image incongruence. However, while based on a large volume of conversations, the analysis covers only four Italian cities.

Destination management organizations (DMOs) should develop their strategy and communication considering internal and external elements: their marketing targets on one side and the local culture and attractions' perceptions on the other. Standard marketing processes (segmenting, targeting, positioning) and theories should be put in place. The application of standard marketing dynamics and studies should push the DMOs to understand that the internally perceived cultural values of the touristic destinations could not be known or joint univocally by the global external customers and that a local promotional activity should start with branding and not commercial activities.

This is the first study to suggest the existence of hype in food tourism promotion of Italian destinations and to provide evidence supporting this argument.

Tourists’ destination image is crucial for visiting intentions. An ancient capital with diverse characteristics is an important component of China’s urban tourism. The purpose of this paper is to address the following questions: what are the differences and commonalities of the perceived destination image of ancient capitals? What makes the difference of the perceived destination image in these cities? Aside from the exterior factors, are there internal factors of cities that influence tourists’ cognition and perception of destination image?

The comment text data of Baidu tourism website were used to determine the differences in the destination images of China’s four great ancient capitals: Beijing, Xi’an, Nanjing and Luoyang. ROST content mining and semantic network analysis were for differences and commonalities of the perceived destination image, and correlation analysis was used to explore the internal factors of cities that influence tourists’ cognition and perception of destination image.

Though the same as ancient capital, the four ancient capitals’ images are far apart; historical interests are the core of tourism experience in ancient capital city; image perception is from physical carrier, history and culture, and human cognition; tourist’ destination affect of ancient capital is most from its history and culture; protecting identity and maintaining daily life are crucial for ancient city tourism.

Previous studies on ancient capitals have focused on the invariable identity of ancient capitals’ destination images, and left a gap on determining from where the invariable identity comes in general and how much it influences destination image. This gap was addressed in this study, by analyzing the destination images of four ancient capitals in China as cases. In this way, this study provided reference to the other ancient cities worldwide.

Encouraging travelers to create value that benefits firms is of great relevance for companies that operate in online contexts. The purpose of this study is to investigate, focusing on online travel agencies, how monetary promotions (i.e. economic incentives) and non-monetary promotions (i.e. draws and contests) conducted through social media enhance customers’ voluntary behaviors (i.e. suggestions, word of mouth, and social media interactions) that go beyond brand choice, which may provide benefit to firms.

The research model draws on the social exchange theory, equity theory and the concept of perceived support – how customers perceive that companies care about their well-being. The authors collect information from 491 users of online travel agencies in Spain and test their hypotheses using partial least squares. They also evaluate the existence of indirect effects.

Promotions developed by companies make customers more likely to perform, voluntarily, the helping behaviors of suggestions, word of mouth and social media interactions, through the influence of perceived support.

Use of a single survey to collect measures and restriction of the sample to Spanish-speaking travelers suggests caution in generalizing the results. Future research could investigate other company-initiated actions and other value-creating behaviors of travelers.

Promotions help develop perceived support for customers, which leads to voluntary, valuable traveler behaviors. Promotions may be also sufficient to trigger some customer behaviors, such as word-of-mouth.

Based on the social exchange and equity theories, this paper investigates the influence of social media promotions on customers’ voluntary behaviors via perceived support.

Based on big data analytical and statistical techniques, this study aims to examine tourists’ shopping experiences at department stores and street markets in Phuket.

A Naïve Bayes machine learning algorithm was used to identify the most frequently used terms in TripAdvisor reviews of both department stores and street markets contributed by the same pool of 729 tourists.

A total of 18 out of 62 terms used were common in reviews of both shopping settings. However, the study found significant differences in the mean use of the 18 common terms and the likelihood of those terms being used in overall positive reviews.

The study’s findings indicate differences in tourist shopping experiences at department stores and street markets. Several concrete recommendations are made, including a greater focus on the linkage to the national characteristic of street markets, and particularly the quality of local fruit, to enhance the tourist shopping experience.

Understanding the differences between shopping malls and street markets from the tourist’s perspective would further enhance the coexistence of shopping malls and street markets in tourism-led growth cities. As such, using reviews of both shopping malls and street markets from an identical pool of tourists, the present study will analyse and compare tourists’ actual shopping experiences, thereby addressing this gap in the research canon via integrated statistical and big data analysis techniques.

Building on a TripAdvisor data for five Mediterranean destinations, namely, Greece, Croatia, Italy, France and Spain, this study analyses the constituents of restaurants' online reputation and their interrelation with destination competitiveness, in particular two Travel and Tourism Competitiveness Index (TTCI) pillars, namely, Prioritisation of Travel and Tourism and Price Competitiveness.

The analysis has revealed that restaurants' online reputation is positively influenced by two factors, namely, Core elements, i.e. cooking, service and price-quality ratio, and Price. Furthermore, the restaurants' online reputation does not influence destination competitiveness (TTCI) directly, but indirectly throughout its main constituents, i.e. service and price. Price is the only variable with significant influence on overall TTCI. Within the sample of these destinations, Balkan countries, i.e. Greece and Croatia, perform very well in terms of their restaurants' online reputation. On the other hand, considering the overall TTCI rating, their competitive positions are substantially lower than those of Italy, France and Spain.

The study provides new insights into the relationship between gastronomic offer and destination competitiveness, and valuable practical implications for destination and hospitality management. Moreover, this study addresses various gaps in existing research on this topic. Specifically, it validates the reputation elements presented online using TripAdvisor data and analyses the impact of electronic Word of Mouth (eWOM) not only as the outcome variable of other constructs, as is the case in the literature, but also as a central construct of the analysis. In doing so, it extends current research on this topic and fills the gap regarding the inclusion of the supply-side stakeholder perspective, which has long been recognised as necessary in any attempts to measure competitiveness.

Social media has fundamentally changed the leisure travel behavior of Singapore residents. The purpose of this paper is to focus on Facebook and its impact on travel behavior. The study focuses on Singapore residents.

A total of 16 input and five output variables were tested, with a sample of 203 Facebook users residing in Singapore. Primary data modeling was done using ADANCO, a structural equation modeling tool that uses composite modeling approach for hypothesis testing. The analysis performed an estimated structural model and then determined the best model fit by measuring reliability, validity and path analysis and estimating model parameters.

Research findings indicate that Facebook has had a strong behavioral influence on the frequency of travel, itinerary planning and social sharing, while it had no impact during the destination-selection stage of travel planning.

Facebook’s influence on leisure travel behavior confirms the theory of planned behavior proposed by Icek Ajzen. From Facebook’s perspective, the outcome of this study is helpful in recommending the best use of the platform for destination providers.

Recent tourism research has adopted social media analytics (SMA) to examine tourism destination image (TDI) and gain timely insights for marketing purposes. Comparing the methodologies of SMA and intercept surveys would provide a more in-depth understanding of both methodologies and a more holistic understanding of TDI than each method on their own. This study aims to investigate the unique merits and biases of SMA and a traditional visitor intercept survey.

This study collected and compared data for the same tourism destination from two sources: responses from a visitor intercept survey (n = 1,336) and Flickr social media photos and metadata (n = 11,775). Content analysis, machine learning and text analysis techniques were used to analyze and compare the destination image represented from both methods.

The results indicated that the survey data and social media data shared major similarities in the identified key image phrases. Social media data revealed more diverse and more specific aspects of the destination, whereas survey data provided more insights in specific local landmarks. Survey data also included additional subjective judgment and attachment towards the destination. Together, the data suggested that social media data should serve as an additional and complementary source of information to traditional survey data.

This study fills a research gap by comparing two methodologies in obtaining TDI: SMA and a traditional visitor intercept survey. Furthermore, within SMA, photo and metadata are compared to offer additional awareness of social media data’s underlying complexity. The results showed the limitations of text-based image questions in surveys. The findings provide meaningful insights for tourism marketers by having a more holistic understanding of TDI through multiple data sources.

This paper aims to explore the causal relationship of user-generated content (UGC) on trust in UGC (TUGC), destination image (DI) and tourist visit intention (VI) guided by the stimulus-organism-response (SOR) theory. This work further examined the mediating effect of TUGC and DI between UGC and VI.

Data were collected through online and personal-administered surveys and randomized sampling. This work employed partial least squares structural equation modeling (PLS-SEM) to test the proposed model empirically.

In line with the SOR Theory, this work found UGCs that induce positive emotions and connection to the users motivate VI rather than UGCs that are predominantly factual. Furthermore, UGCs are considered reliable, authentic and less biased than brand-generated content. The findings of this work contribute to the theoretical understanding of UGC to VI in a destination.

This work proposes that destination marketers prioritize UGC that evokes positive emotions and connections with users, as it is more effective in encouraging VI. Strategies such as incentivizing content creators, improving online presence and engaging influencers can maximize UGC. Enhancing online traffic quality, visibility and interaction and implementing content policies are crucial for UGC's effectiveness. Marketers should align destination products with tourists' interests and collaborate with influencers for affiliate marketing to increase tourist-generated UGC. Furthermore, improved connectivity encourages UGCs about the destination.

In tourism marketing, UGC has become a valuable information source for tourists in making informed travel decisions. UGC is a tourist-generated content that offers factual information and authentic experiences through images, videos or text posted through social media platforms. UGC is considered more reputable than travel firms and the mainstream media as an information source. Due to the limited works on UGC in the literature, the influence of UGC on tourists' VIs has remained unexplored at the time of writing. This work bridges this gap by empirically examining the impact of UGC on Gen Z tourists' VI guided by the SOR theory.

World Tourism Organization (WTO) recognizes image as the main aspect to be considered by a destination in its promotion and marketing process. Cities try to build valued and recognized images, established from an identity defined based on their own values. One of the strategies adopted for this construction is to hold events, through which it is possible to promote tourism, move the economy, improve the infrastructure, change the image and influence intentions to visit the destination. From the point of view of supply and demand, theorists have proposed two categories of destination image: the projected image and the perceived image. In this context, the objective of the research was to propose a model for measuring the Projected and Perceived Image through the Organizational Identity of the Volvo Ocean Race Brazil.

With a quali-quantitative approach, the study universe is composed of in-depth interviews with the main members of the organizing committee, documentary and netnographic analysis of the event's social networks. For the analysis and interpretation of qualitative data, the collective subject discourse was used. Documentary and netnographic analysis were by means of deductive content analysis and correspondence analysis.

The results supported the three secondary hypotheses of the research, leading to confirm the central hypothesis that the constructed organizational identity, projected by the image, is perceived by visitors to the event studied.

It is understood as fundamental the expansion of studies regarding projected and perceived image, identity and the possibility of its application in tourist events, as social representations, as support also for the definition of management and marketing strategies.