Using Social Media in Tourist Sentiment Analysis: A Case Study of Andalusia during the Covid-19 Pandemic

Computational linguistics based text emotion analysis using enhanced beetle antenna search with deep learning during COVID-19 pandemic

Computational intelligence and nature-inspired computing have changed the way biologically and linguistically driven computing paradigms are made. In the last few decades, they have been used more and more to solve optimisation problems in the real world. Computational linguistics has its roots in linguistics, but most of the studies being done today are led by computer scientists. Data-driven and machine-learning methods have become more popular than handwritten language rules, which shows this shift. This study uses a new method called Computational Linguistics-based mood Analysis using Enhanced Beetle Antenna Search with deep learning (CLSA-EBASDL) to tackle the important problem of mood analysis during the COVID-19 pandemic. We sought to determine how people felt about the COVID-19 pandemic by studying social media texts. The method is made up of three main steps. First, data pre-processing changes raw data into a shape that can be used. After that, word embedding is done using the 'bi-directional encoder representations of transformers (BERT) process. An attention-based bidirectional long short-term memory (ABiLSTM) network is at the heart of mood classification. The Enhanced Beetle Antenna Search (EBAS) method, in particular, fine-tunes hyperparameters so that the ABiLSTM model works at its best. Many tests show that the CLSA-EBASDL method works better than others. Comparative studies show that it works, making it the best method for analysing opinion during the COVID-19 pandemic.

Effects of social media tourism information quality on destination travel intention: Mediation effect of self-congruity and trust

The asymmetry of tourism information makes social media an important information source. Previous research has been conducted on the influence of tourist-generated content on tourism consumption behavior, but few studies have concentrated on the mechanism of tourism information quality on consumers' travel intention in the social media environment. Adopting the Elaboration Likelihood Model, this paper aims to investigate how the information quality of social media affects consumers' travel intention rationally and emotionally and the moderation effect of tourists' prior knowledge. The empirical results indicate that the quality of social media information positively affects travel intention and self-congruity and trust mediates the relationship between the quality of social media information and travel intention. Moreover, this study identified that tourists' prior knowledge negatively modifies the relationship between information quality and self-congruity in line with the proposed hypotheses. The research explores the influence mechanism of tourist-generated content quality on consumers' travel intention, which benefits destination management and content marketing.

Twitter data sentiment analysis of tourism in Thailand during the COVID-19 pandemic using machine learning

The coronavirus disease 2019 (COVID-19) pandemic has severely affected Thailand's economy, which relies heavily on tourism. In this study, we labeled the sentiment and intention classes of English-language tweets related to tourism in Bangkok, Chiang Mai, and Phuket. Then, the accuracy of three machine learning algorithms (decision tree, random forest, and support vector machine) in predicting the sentiments and intentions of the tweets was investigated. The support vector machine algorithm provided the best results for sentiment analysis, with a maximum accuracy of 77.4%. In the intention analysis, the random forest algorithm achieved an accuracy of 95.4%. In a subsequent preliminary qualitative content analysis, the top 10 words found in each sentiment and intention class were gathered to provide insights and suggestions to help increase tourism in Thailand. The results of this study suggest that to help restore tourism in Thailand, tourist destinations, natural attractions, restaurants, and nightlife should be promoted. In addition, the two main concerns of tourists to Thailand should be addressed: COVID-19 and current political tensions.

Could Social Bots’ Sentiment Engagement Shape Humans’ Sentiment on COVID-19 Vaccine Discussion on Twitter?

During the COVID-19 pandemic, social media has become an emerging platform for the public to find information, share opinions, and seek coping strategies. Vaccination, one of the most effective public health interventions to control the COVID-19 pandemic, has become the focus of public online discussions. Several studies have demonstrated that social bots actively involved in topic discussions on social media and expressed their sentiments and emotions, which affected human users. However, it is unclear whether social bots’ sentiments affect human users’ sentiments of COVID-19 vaccines. This study seeks to scrutinize whether the sentiments of social bots affect human users’ sentiments of COVID-19 vaccines. The work identified social bots and built an innovative computational framework, i.e., the BERT-CNN sentiment analysis framework, to classify tweet sentiments at the three most discussed stages of COVID-19 vaccines on Twitter from December 2020 to August 2021, thus exploring the impacts of social bots on online vaccine sentiments of humans. Then, the Granger causality test was used to analyze whether there was a time-series causality between the sentiments of social bots and humans. The findings revealed that social bots can influence human sentiments about COVID-19 vaccines. Their ability to transmit the sentiments on social media, whether in the spread of positive or negative tweets, will have a corresponding impact on human sentiments.

Using social media listening and data mining to understand travellers' perspectives on travel disease risks and vaccine-related attitudes and behaviours

BACKGROUND

Travellers can access online information to research and plan their expeditions/excursions, and seek travel-related health information. We explored German travellers' attitude and behaviour toward vaccination, and their travel-related health information seeking activities.

METHODS

We used two approaches: web 'scraping' of comments on German travel-related sites and an online survey. 'Scraping' of travel-related sites was undertaken using keywords/synonyms to identify vaccine- and disease-related posts. The raw unstructured text extracted from online comments was converted to a structured dataset using Natural Language Processing Techniques. Traveller personas were defined using K-means based on the online survey results, with cluster (i.e. persona) descriptions made from the most discriminant features in a distinguished set of observations. The web-scraped profiles were mapped to the personas identified. Travel and vaccine-related behaviours were described for each persona.

RESULTS

We identified ~2.6 million comments; ~880 k were unique and mentioned ~280 k unique trips by ~65 k unique profiles. Most comments were on destinations in Europe (37%), Africa (21%), Southeast Asia (12%) and the Middle East (11%). Eight personas were identified: 'middle-class family woman', 'young woman travelling with partner', 'female globe-trotter', 'upper-class active man', 'single male traveller', 'retired traveller', 'young backpacker', and 'visiting friends and relatives'. Purpose of travel was leisure in 82-94% of profiles, except the 'visiting friends and relatives' persona. Malaria and rabies were the most commented diseases with 12.7 k and 6.6 k comments, respectively. The 'middle-class family woman' and the 'upper-class active man' personas were the most active in online conversations regarding endemic disease and vaccine-related topics, representing 40% and 19% of comments, respectively. Vaccination rates were 54%-71% across the traveller personas in the online survey. Reasons for vaccination reluctance included perception of low risk to disease exposure (21%), price (14%), fear of side effects (12%) and number of vaccines (11%).

CONCLUSIONS

The information collated on German traveller personas and behaviours toward vaccinations should help guide counselling by healthcare professionals.

Social Media in Sustainable Tourism Recovery

In the light of the COVID-19 pandemic, it is crucial to manage tourist destinations to allow the recovery of tourism on the one hand and reduce its negative impact on the environment and the local community on the other. Information provided via social media (SM) by both residents and tourists can help restart tourism. This paper identifies ways of sharing travel experiences by tourists on social media. The research was conducted in Poland on a sample of 271 respondents from each generation using questionnaires. Results showed that the way tourists use SM during and after their trip differs by generation and gender what could be used in promoting responsible behaviour for sustainable tourism. Differences between generations can be observed in behaviours such as ongoing planning the trip, obtaining information about the place to stay, keeping a photo album for friends, and writing reviews. Moreover, more often than men, women use SM to obtain information about the place of stay and share their impressions of the trip by sending MMS or emails. Tracking tourists’ travel behaviour on social media will allow city managers to gather information and respond to their needs and expectations and ensure effective urban management and city promotion.

Functions of Currency before and after COVID-19: Is Bitcoin Sustainable for Tourism?

The specific purposes of this study are (1) to analyze whether there is a difference in the recognition of tourists in the functions of currency according to the type of currency spent by tourists before and after COVID-19; (2) to identify why tourists employ Bitcoin among the functions of currency; and (3) to identify tourists’ intentions to continue to using Bitcoin according to the degree of recognition of the function of virtual currency in Korea. Both quantitative and qualitative methods, including a one-way analysis of variance, a paired t-test, a simple regression analysis, and an in-depth interview, were employed. One of the conclusions implies that tourists in a tourism destination still prefer paying with cash rather than paying with card or virtual currency. Moreover, the study discovered that tourists count more on virtual currency and prepare for a future with virtual currency when a factor that has an unstable impact on social economy, such as COVID-19, happens. As to the functions of Bitcoin, tourists appeared to place a great importance on its storage, value, satisfaction, convenience, and investment. Additionally, the study showed that tourists intend to continue using Bitcoin due to the same satisfaction, convenience, and investment attributed to Bitcoin. More discussion and implications were provided.

Sustainable Determinants That Affect Tourist Arrival Forecasting

This study considers diversification effects and significant influences on tourist arrivals as a vital export direction. Different quantitative methods, namely a cointegrated-autoregressive model, panels, sentiment and sensitivity analysis, were used in this study. The time-series data for Croatia and Slovenia were isolated from several secondary sources. The variables examined in this approach are tourist arrivals, precipitations, sunny days, earthquakes, microbes and CO2 emissions. The study results showed that there is a severe negative effect on tourist arrivals defined by viruses. Moreover, there is a significant decisive effect of weather conditions on tourist arrivals. Nevertheless, it is necessary to move past Covid-19 pandemic discussions to yield more accurate tourism supply forecasts, while demand is already somehow low since the beginning of 2020. The primary significance is to develop a broader thinking about the impacts of CO2 emissions on the tourism escorted to official tourist websites.