The main objective of this exploratory study is to identify the social, economic, environmental and cultural factors related to the sustainable care of both environment and public health that most concern Twitter users. With 336 million active users as of 2018, Twitter is a social network that is increasingly used in research to get information and to understand public opinion as exemplified by Twitter users. In order to identify the factors related to the sustainable care of environment and public health, we have downloaded n = 5873 tweets that used the hashtag #WorldEnvironmentDay on the respective day. As the next step, sentiment analysis with an algorithm developed in Python and trained with data mining was applied to the sample of tweets to group them according to the expressed feelings. Thereafter, a textual analysis was used to group the tweets according to the Sustainable Development Goals (SDGs), identifying the key factors about environment and public health that most concern Twitter users. To this end, we used the qualitative analysis software NVivo Pro 12. The results of the analysis enabled us to establish the key factors that most concern users about the environment and public health such as climate change, global warming, extreme weather, water pollution, deforestation, climate risks, acid rain or massive industrialization. The conclusions of the present study can be useful to companies and institutions that have initiatives related to the environment and they also facilitate decision-making regarding the environment in non-profit organizations. Our findings will also serve the United Nations that will thoroughly review the 17 SDGs at the High-level Political Forum in 2019.

In recent years, electronic word of mouth (e-WOM) has been widely used by consumers on different online platforms. Numerous studies have emphasized the growing importance of e-WOM for the consumer decision-making process, particularly in the tourist sector. There are various factors that will influence the adoption of e-WOM by the users but among all these factors, credibility is of paramount importance. Changes in the platform, new consumer trends, and possible fake information require a continuous update and analysis of the factors that can influence the e-WOM perceived credibility and e-WOM adoption on TripAdvisor and other social tourism platforms. In the present study, we analyzed the following five factors that can impact e-WOM perceived credibility and e-WOM adoption: (1) volume of e-WOM; (2) source credibility, (3) rate extremism, (4) consumer involvement, and (5) perceived e-WOM credibility. For the analysis, the Elaboration Likelihood Model (ELM) and PLS-SEM were used. The sample consisted of a total of 221 participants who responded to the questionnaire. The results revealed that, with the exception rate extremism, the four remaining factors have a significant impact on e-WOM perceived credibility and adoption. Therefore, these factors are important drivers of e-WOM perceived credibility resulting in e-WOM adoption. The results of the present study provide meaningful practical implications for hotel or social tourism platforms managers in terms of possible strategies to improve their online reputation.

Although large amounts of data are now available to companies, mere possession of these data is not sufficient, and, for better business decisions, it is necessary to perform thorough data analysis. Nowadays, Social Networks Services (SNS) have become important data sources. The rapid growth of SNS has led to their wide use in various research trends in social sciences. In the present study, we aim to enhance the current understanding of the possibilities offered by social data for brand communication analysis in the financial sector. To this end, a traditional methodology and a digital methodology are used to investigate the brand image of financial entities. The traditional methodology is the Periodic Evaluation of the Image (PEI). The digital methodology is sentiment analysis, a machine learning technique for Big Data analytics in social sciences using an algorithm developed in Python. The data are analyzed using both methodologies, and then their results are compared. The findings suggest that, while the results obtained using the method based on Big Data are consistent with the results obtained with the traditional methodology, the former method allows for an easier and faster data analysis. The limitations of the present study relate to the size of the sample, the studied sector, and the scope of reviewed literature.

The main aim of this study was to identify the key indicators related to environmental management and sustainability of hotels as perceived by travelers during their trips. The methodology used was a sentiment analysis with an algorithm developed in Python trained with data mining and machine learning, with the MonkeyLearn library in the hotel industry sector under the eWOM model (e-Word of Mouth). The results with negative, positive and neutral feelings were submitted to a textual analysis with the qualitative analysis software Nvivo Pro 12. The sample consisted of the 25 best hotels in Switzerland according to Traveler’s Choice from TripAdvisor ranking 2018 that draws from more than 500,000 reviews. For data extraction, we connected to the TripAdvisor API, obtaining a sample of n = 8331 reviews of the hotels that made up the ranking. The results of the study highlight the key factors related to environmental management detected by travelers during their stay in hotels and can be meaningfully used by managers or hotel managers to improve their services and enhance the value provided by their policies of sustainability and respect for the environment. The limitations of the present study relate to the size of the sample and the number of hotels included in the present analysis.

Electronic word of mouth (eWOM) has been widely used by most consumers on different digital platforms. This review aimed to obtain further insights into online consumer behavior through social networking sites and online reviews sites to help tourism businesses develop sustainable eWOM strategies. To this end, an exploratory study was developed to analyze available literature on eWOM strategies and online consumer behavior. The systematic literature review analysis focused on the following two main topics: (i) tourism and (ii) eWOM. The scientific database, Web of Science, was used to collect relevant literature on the subject. The search terms “Tourism” and “eWOM” were used. Searching the database, Web of Science, yielded a total of 124 articles; upon application of different filters, a total of 14 studies were included in the final dataset. The results of the present study provide new insights into consumer behavior for social sciences and businesses for the adoption of sustainable strategies to increase the influence of eWOM on the tourism industry.

The Black Friday event has become a global opportunity for marketing and companies’ strategies aimed at increasing sales. The present study aims to understand consumer behavior through the analysis of user-generated content (UGC) on social media with respect to the Black Friday 2018 offers published by the 23 largest technology companies in Spain. To this end, we analyzed Twitter-based UGC about companies’ offers using a three-step data text mining process. First, a Latent Dirichlet Allocation Model (LDA) was used to divide the sample into topics related to Black Friday. In the next step, sentiment analysis (SA) using Python was carried out to determine the feelings towards the identified topics and offers published by the companies on Twitter. Thirdly and finally, a data-text mining process called textual analysis (TA) was performed to identify insights that could help companies to improve their promotion and marketing strategies as well as to better understand the customer behavior on social media. The results show that consumers had positive perceptions of such topics as exclusive promotions (EP) and smartphones (SM); by contrast, topics such as fraud (FA), insults and noise (IN), and customer support (CS) were negatively perceived by customers. Based on these results, we offer guidelines to practitioners to improve their social media communication. Our results also have theoretical implications that can promote further research in this area.