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COVID-19 has wreaked havoc worldwide. Schools have escaped neither the pandemic nor its consequences. Indeed, by April 2020, schools had been suspended in 189 countries, affecting 89% of learners globally. While the Australian government has implemented variously effective health and economic policies in response to COVID-19, their inability to agree with states on education policy during the pandemic caused considerable confusion and anxiety. Accordingly, this study analyses 3 weeks of Tweets during April, leading up to the beginning of term 2, during the height of Government policy incongruity. Findings confirm a wide and rapidly changing range of public responses on Twitter. Nine themes were identified in the quantitative analysis, and six of these (positive, negative, humorous, appreciation for teachers, comments aimed at Government/politicians and definitions) are expanded upon qualitatively. Over the course of 3 weeks, the public began to lose its sense of humour and negative tweets almost doubled. [ABSTRACT FROM AUTHOR]

Insights into the activity preferences of specific tourist groups are crucial for tourism practitioners in developing appropriate travel packages that attract tourists and meet their expectations. Tourist activities across different countries often vary depending on what these destinations can offer. Existing attempts to obtain comprehensive information on tourist activities across multiple countries often fail because of the limitations of traditional data collection approaches, which are heavily reliant on surveys and questionnaires. This study proposes to address the challenge by introducing a new type of data called venue-referenced social media data (VR-SMD), which can effectively determine various tourist activities and the temporal preferences for each activity at different destinations. We demonstrate the effectiveness of VR-SMD using a case study that focuses on Malaysian outbound travelers. Their cross-cultural differences from another travel group, namely, Thai outbound travelers, are highlighted through a comparative analysis. Analysis results provide valuable insights for various tourism applications. [ABSTRACT FROM AUTHOR]

Travel itineraries are employed in tourism research to study tourist activities for various applications. However, the potentials of such itineraries in providing insights into the activity preferences of tourists have not been explored because of the complexity of travel information. In this paper, a new approach based on probabilistic topic modeling with latent Dirichlet allocation is introduced for travel itinerary analysis and representation. Capable of revealing the implicit preferences of tourists, the new approach enables topic modeling to be applied in itinerary analysis. We demonstrate its effectiveness through a case study of outbound travel behavior analysis on a large-scale travel itinerary data set. Activity profiles of various itinerary types at different destinations are revealed. The results are useful for travel and tourism managers in developing travel and tour packages. The general features of the proposed method can be applied into different tourism contexts and travel itinerary formats for wide applications. • A new method for travel itinerary analysis based on probabilistic topic modeling is introduced. • The method can reveal implicitly tourist activity preferences in complex travel patterns. • A case study of outbound travel behavior demonstrates the effectiveness of the method. • Analyses are carried out on travel itineraries constructed from Foursquare venue check-ins. • Findings provide valuable implications for travel and tour package development. [ABSTRACT FROM AUTHOR]

Approaches to traditional travel diary construction rely on tourist participation and manual recording; hence, they are not only time-consuming but also limited in the scale and the number of samples. Online social network platforms have been used as alternative data sources for capturing the movements and travel patterns of tourists at a large scale. However, they fail to provide detailed contextual information on tourist activities for further analysis. In this paper, we present a new approach to travel diary construction based on the venue check-in data available in mobile social media with rich information on locations, time, and activities. Our case study focuses on the inbound tourism in Hong Kong using a data set composed of 17,355 check-ins generated by 600 tourists. We demonstrate how the proposed travel diary can provide useful practical implications for applications in location management, transportation management, impact management, and tourist experience promotion among others. [ABSTRACT FROM AUTHOR]

Travel diaries have been widely used by tourism researchers to gain insights into the travel behaviors of travelers because of their rich information. However, their potentials have not been fully utilized due to the unawareness of the utility information hidden within. Existing works have mainly focused on identifying popular travel patterns in the travel diaries, while overlooking their potential profitable benefits. To fill this gap, we introduce the concept of "utility" into the analysis of travel diaries and present high-utility pattern mining technique for the effective discovery of highly valuable travel patterns. We demonstrate the benefit of this approach through a case study using a large-scale travel diary data set constructed from venue check-in data. The findings are useful for tourism managers to develop valuable travel packages. The presented approach is general and can be applied to various tourism contexts to explore highly profitable travel and purchasing patterns. • The concept of "utility" is introduced into the analysis of travel patterns. • High utility pattern mining technique is presented to discover highly profitable travel patterns. • The method is demonstrated using a case study of outbound travel behavior. • Analysis on a large scale travel diary data set constructed from social media data is presented. • The method can be applied to various tourism contexts for highly profitable pattern discovery. [ABSTRACT FROM AUTHOR]