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Geopolitics is shaping the international education landscape. International education has trationally been used as a tool to boost transnational cooperation, foster multilateral and global ties, and reduce tensions between nations. Such a role has been eroded and international education has been weaponised in the context of escalating political turbulences and disputes over the COVID-19 pandemic. In particular, the relationship between Australia and China, with international student flows interrupted due to COVID-19, is overshadowed by escalating geopolitical tensions in the Indo-Pacific region. Based on a qualitative study, this article examines stakeholders' views on the responses of the Australian international education sector and universities to emerging geopolitical tensions. The conjuncture of geopolitics, COVID-19 and Australia's former government responses magnified a sense of crisis for universities and the international education sector as it was at risk because of their financial reliance on international students. Based on the findings, recommendations are made for the framing of a reciprocal, coordinated, responsive and empathetic international education sector to mitigate geopolitical risks and ensure more sustainable and ethical development for the sector.

Understanding and being able to measure, analyze, compare, and contrast the image of a tourism destination, also known as tourism destination image (TDI), is critical in tourism management and destination marketing. Although various methodologies have been developed, a consistent, reliable, and scalable method for measuring TDI is still unavailable. This study aims to address the challenge by proposing a framework for a holistic measure of TDI in four dimensions, including popularity, sentiment, time, and location. A structural model for TDI measurement that covers various aspects of a tourism destination is developed. TDI is then measured by a comprehensive computational framework that can analyze complex textual and visual data on a large scale. A case study using more than 30,000 images, and 10,000 comments in relation to three tourism destinations in Australia demonstrates the effectiveness of the proposed framework. [ABSTRACT FROM AUTHOR]

Understanding how people engage in daily activities within a region can provide valuable information for smart city planners and strategic partners to use to assist in their decision‐making processes. Such insights may relate to economic activities, sustainable city design, environmental impacts, and responses to climate change, contributing to the improvement in the quality of human life. Considerable attention is recently directed towards smart city initiatives that benefit majority of people, rather than projects that cater to the political, architectural, or vanity needs of a minority. However, understanding citizen requirements, behaviors, and opinions is difficult, and requires the use of technology and appropriate information sources. While social‐media big data have provided opportunities to develop evidence‐based insights into human daily activities, effective analytical methods to harness these opportunities remain in development. We propose a new analytical method to provide a deeper understanding of citizen activities by constructing building blocks in their activity storylines, with analysis of these storylines providing evidence‐based insights into their activities. Results demonstrate the usefulness of our method to smart city planners and strategic partners, providing invaluable insights to assist them in making decisions regarding sustainable smart city development. [ABSTRACT FROM AUTHOR]

With the prevalence of social media and Web 2.0, online visual contents such as photos or videos have quickly evolved into one popular information-disseminating channel for hotel managers and travelers. The current study aims to obtain a comprehensive understanding of the preconceptions as reflected in online photos posted by travelers. This paper presents a novel approach to online photo content analysis based on deep learning theory and computer vision framework, which can comprehensively analyze the content of large-scale photo datasets. We demonstrate and evaluate this approach through a case study, wherein we analyze over 53,000 photos collected from hotel review platform, TripAdvisor. We identified interesting differences in the contents of photos posted by hotel managers and travelers, including the differences in photo contents between low- and high-rating hotels. Our findings provide valuable implication for hotel marketing using visual assets. [ABSTRACT FROM AUTHOR]

Understanding the differences and similarities in the activities of tourists from various cultures is important for tourism managers to develop appropriate plans and strategies that could support urban tourism marketing and managements. However, tourism managers still face challenges in obtaining such understanding because the traditional approach of data collection, which relies on survey and questionnaires, is incapable of capturing tourist activities at a large scale. In this article, we present a method for the study of tourist activities based on a new type of data, venue check-ins. The effectiveness of the presented approach is demonstrated through a case study of a major tourism country, France. Analysis based on a large-scale data set from 19 tourism cities in France reveals interesting differences and similarities in the activities of tourists from 14 markets (countries). Valuable insights are provided for various urban tourism applications. [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]