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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]

High volumes of tourists often pose a threat to tourism and decrease the quality of life for local residents, particularly in attractive urban tourism places. Yet, to date only a few solution-oriented studies have attempted to alleviate the overtourism problems and to improve the resident-tourist relationship. This study aims to present potential solutions, based on data analytics. Combining venue-referenced social media data with topic modelling from a case study in Paris, this research reveals both similarities and differences in the temporal and spatial activity patterns of tourists and residents. Results offer strategic support to tourism planners on how to manage over-crowded urban tourism hotspots, which consequently facilitate the improvement of the resident–tourist relationship and improve destination attractiveness in the long run. Results further indicate that the exchange of social media-based information for residents and tourists are part of the practice-based solution for better sustainable tourism planning. [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]

This study introduces topic modelling into the analysis of theme park online reviews to determine visitor behaviour and experiences. An exploratory analysis involving major Disneyland theme parks is presented using a large-scale review data set. A comprehensive list of the topics discussed by visitors when visiting Disneyland theme parks is constructed. Insights into the interests and concerns of various visitor groups across theme parks are revealed. The proposed approach and findings are beneficial to support theme park managers in understanding visitors' perception, through which effective marketing and improvement plans can be developed to attract and retain future customers. [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]

The availability of location-based social media (LBSM) presents various opportunities for tourism researchers and businesses to understand and enhance the traveller experience. However, privacy concerns can prevent users from sharing their location data, impeding the future development of LBSM applications. Privacy issues of LBSM remain under investigation in the tourism literature probably because social media platforms are often assumed to feature good security mechanisms. This paper argues that risks of privacy disclosure exist not from direct access to private data but indirectly through seemingly harmless data published by users. We present a social link mining technique and demonstrate how private and sensitive information in the form of social relationships between travellers can be revealed. The findings can support managers and researchers in realizing the potential privacy risks of LBSM so that effective strategies can be developed to protect users and promote the gains that LBSM can bring. [ABSTRACT FROM AUTHOR]

Zoning is one approach to managing human occurrence and reducing deleterious interactions between humans and wildlife. We investigated the occurrence of humans, and the responses of eight waterbird species to humans, at a major wetland/treatment plant/birdwatching destination. Human occurrence in three zones ('open birdwatching', 'limited birdwatching' and 'restricted access') was monitored using GPS tracking of visitor vehicles, surveys, geotagged social media uploads and remotely triggered cameras (on primary and secondary roadways). A higher diversity (but not frequency) of vehicle types and more walkers, more social media uploads, and greater usage occurred in zones in which birdwatching was permitted. Vehicles were less common and diverse on secondary roads, suggesting accessibility influenced human occurrence. Bird responsiveness to humans was similar across zones, perhaps because people were ubiquitous or because birds were mobile. Wildlife disturbance studies which use space-experience substitution designs are cautioned to test their assumptions regarding patterns of human visitation. [ABSTRACT FROM AUTHOR]

Location analytics offers statistical analysis of any geo- or spatial data concerning user location. Such analytics can produce useful insights into the attractions of interest to travellers or visitation patterns of a demographic group. Based on these insights, strategic decision-making by travel marketing agents, such as travel package design, may be improved. In this paper, we develop and evaluate an original method of location analytics to analyse travellers' social media data for improving managerial decision support. The method proposes an architectural framework that combines emerging pattern data mining techniques with image processing to identify and process appropriate data content. The design artefact is evaluated through a focus group and a detailed case study of Australian outbound travellers. The proposed method is generic, and can be applied to other specific locations or demographics to provide analytical outcomes useful for strategic decision support. [ABSTRACT FROM AUTHOR]

This research introduces the use of venue check-ins, which are available on mobile social media platforms, as an additional data source to study tourist behaviors in gaming destinations. The practical value of venue check-ins is demonstrated through a case study of Macau, a popular gaming and cultural destination. The analyses provide an overview of the travel patterns of tourists to gambling and non-gambling venues and highlight the differences between gambling and non-gambling tourists. The introduced approach is beneficial to tourism managers in understanding tourist behaviors and interests and, thus, improves the management and development of gaming destinations. [ABSTRACT FROM AUTHOR]

Identification of hotel competitiveness is crucial for hotel managers in developing effective marketing strategies and promoting their business. A common practice in representing hotel competitiveness is to advertise hotel features with high customer ratings as selling points. However, this approach is ineffective to clearly distinguish top-tier hotels with similar high ratings, while low-tier hotels are disadvantaged because of their low ratings. This study presents a new approach for hotel competitiveness evaluation by identifying the unique aspects that combine multiple features. The proposed approach could distinguish a hotel from its counterparts, rather than rely solely on the high ratings of hotel features. We introduce a data mining technique to automatically discover the distinct aspects of a hotel based on the ratings of hotel features. The proposed method is beneficial to researchers and hotel managers in evaluating hotel competitiveness and identifying critical features that need improvement to considerably attract potential customers. [ABSTRACT FROM AUTHOR]

Dining is an essential tourism component that attracts significant expenditure from tourists. Tourism practitioners need insights into the dining behaviors of tourists to support their strategic planning and decision making. Traditional surveys and questionnaires are time consuming and inefficient in capturing the complex dining behaviors of tourists at a large scale. Thus far, the understanding about the dining preferences and opinions of different tourist groups is limited. This article aims to fill the void by presenting a method that utilizes online restaurant reviews and text processing techniques in analyzing the dining behaviors of tourists. The effectiveness of the proposed method is demonstrated in a case study on international tourists visiting Australia using a large-scale data set of more than 40,000 restaurant reviews made by tourists on 2,265 restaurants. The proposed method can help researchers gain comprehensive insights into the dining preferences of tourists. [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]

This research introduces online travel photos published on social media platforms as a complementary data resource to examine the behavior and experience of museum visitors. The practical value of online travel photos is demonstrated through a case study of popular Hong Kong museums, particularly by using the photo content and metadata available from the Flickr platform. The proposed approach is a generic method for understanding museum visitor behavior and preferences, and supports museum practitioners in developing improved products for visitors. The case study findings are particularly beneficial for tourism managers, especially those in Hong Kong, in promoting and attracting tourists to visit local museums. [ABSTRACT FROM AUTHOR]

Because of the inefficiency in analyzing the comprehensive travel data, tourism managers are facing the challenge of gaining insights into travelers’ behavior and preferences. In most cases, existing techniques are incapable of capturing the sequential patterns hidden in travel data. To address these issues, this article proposes to analyze the travelers’ behavior through geotagged photos and sequential rule mining. Travel diaries, constructed from the photo sequences, can capture comprehensive travel information, and then sequential patterns can be discovered to infer the potential destinations. The effectiveness of the proposed framework is demonstrated in a case study of Australian outbound tourism, using a data set of more than 890,000 photos from 3,623 travelers. The introduced framework has the potential to benefit tourism researchers and practitioners from capturing and understanding the behaviors and preferences of travelers. The findings can support destination-marketing organizations (DMOs) in promoting appropriate destinations to prospective travelers. [ABSTRACT FROM AUTHOR]

Certain tasks in image processing require the preservation of fine image details, while applying a broad operation to the image, such as image reduction, filtering, or smoothing. In such cases, the objects of interest are typically represented by small, spatially cohesive clusters of pixels which are to be preserved or removed, depending on the requirements. When images are corrupted by the noise or contain intensity variations generated by imaging sensors, identification of these clusters within the intensity space is problematic as they are corrupted by outliers. This paper presents a novel approach to accounting for spatial organization of the pixels and to measuring the compactness of pixel clusters based on the construction of fuzzy measures with specific properties: monotonicity with respect to the cluster size; invariance with respect to translation, reflection, and rotation; and discrimination between pixel sets of fixed cardinality with different spatial arrangements. We present construction methods based on Sugeno-type fuzzy measures, minimum spanning trees, and fuzzy measure decomposition. We demonstrate their application to generating fuzzy measures on real and artificial images. [ABSTRACT FROM AUTHOR]

Variations between journal rankings may cause confusion. As such, prior attempts were made to compare and evaluate journal ranking criteria for obtaining insightful knowledge on how different research communities have ranked journals. However, existing approaches are unable to model the journal ranking process closely enough as they are incapable of considering the relationship between multiple criteria simultaneously. In this paper, we address the challenges by introducing the Choquet Integral (CI) for evaluating journal ranking criteria. The new approach is able to account for interactions between criteria in relation to overall ranking score, using a fuzzy measure in its computation. Its properties, the Shapley value and the Interaction index, allow for good representations of importance and interactions between criteria. We demonstrate the efficiency of the CI through a case study of journal ranking lists in tourism and service journals. [ABSTRACT FROM PUBLISHER]