In parallel with the major developments witnessed in information technologies, social media, supported by its broad area of usage, is gaining significance in every field, with the tourism sector being no exception in this regard. The transfer of changing marketing strategies via digital channels into social media has transformed the way customers interact with the tourism sector, having now the ability to access the comments of other consumers via social media, and adjusting their preferences accordingly. In this study, the impact of the content analysis on the star rating given to hotels is examined with a "Panel Data Analysis" of all the hotels in Istanbul that have received a maximum of 25 reviews on the TripAdvisor website, with a total of 12,000 comments assessed. It was found that the location of the hotel, the access to transport facilities, the food and beverage concept, the quality of staff/service, and the cleanliness of the facilities all affected the star ratings given to hotels; while the architectural structure and the recommendations of other guests had a lesser effect. It was further noted that entertainmentanimation programs had little effect on guest preferences.

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Moving marketing strategies, which are changed through digital channels, into the social media environment, has led to changes in customer preferences in the tourism sector and is affected by the comments made in social media. Despite the fact that numerous studies have examined the impact of online customer comments on purchasing behaviour, most of these studies have used descriptive statistics and have ignored the empirical estimations. This study is aimed at examining the influence of the criteria obtained by content analysis on the star ratings given to the hotels based on the comments about the hotel on the TripAdvisor site. In this context, Mediterranean region hotels, which hold an important place in the tourism sector of Turkey, have been viewed, and the 25 hotels with the highest number of comments on the TripAdvisor site were selected, and 9000 comments from these hotels as well as the topic of the study were analysed with panel data methodology. As a result of the analyses made, it was determined that the criteria of the location and the accessibility of the hotels, the food and beverage concept presented by the hotels, the quality of the personnel–service, the cleaning elements, and the entertainment–animation programs were influential on the star ratings given to the hotels. It was also found that the architectural structure of the hotels and the other customers’ recommendation criteria are less important than the star ratings given to the hotel when compared to other specified criteria.

Grit, a non-cognitive skill that indicates perseverance and passion for long-term goals, has been shown to predict academic achievement. This paper provides evidence that grit also predicts student outcomes during the challenging period of the Covid-19 pandemic. We use a unique dataset from a digital learning platform in the United Arab Emirates to construct a behavioral measure of grit. We find that controlling for baseline ability, students who were grittier according to this measure before the pandemic, register lower declines in math and science scores during the coronavirus period. Using machine learning, behavioral data in the platform prior to the pandemic can explain 77% of the variance in academic resilience. A survey measure of grit of the same students, on the other hand, does not have significant predictive power over performance changes. Our findings have implications for interventions on non-cognitive skills, as well as how data from digital learning platforms can be used to predict student behavior and outcomes, which we expect will be increasingly relevant as AI-based learning technologies become more common.