Starting with the current situation, problems, and future of tourism big data in China, this article explores the development of big data for China’s tourism industry from four aspects: the history of its development, market entities and construction models, data composition of tourism big data, and forms of products and services. It further analyzes the five leading issues that hinder the development of using big data in tourism: a lack of top-level planning, unclear operation modes, difficulties in data sharing between the government and tourism enterprises, insufficient interaction between academics and tourism enterprises, and inadequate talent training. Based on these factors and from the perspective of integrated culture and tourism, this study explores the future development of big data in tourism. For the first time, this article systematically and theoretically investigates the use of big data for China’s tourism and proposes suggestions for feasible models from the perspective of its development.

This paper reviews the research trend of tourism user-generated content in the Chinese context via bibliometric analysis of content in SSCI-indexed journals concerning the hotel industry and tourism management and proposes future research directions in this field. Research has shown that universities such as the Hong Kong Polytechnic University, the Harbin Institute of Technology, Sun Yat-sen University, and Nankai University have made significant academic contributions in this research field. Online reviews on travel websites, such as TripAdvisor, are the most commonly used data sources for this type of research, and scholars have paid particular attention to Beijing, Hong Kong, Shanghai, and Guangzhou, among other popular tourist cities. The predominant research topics include the perception of destination image, the impact of user-generated content on the performance of tourism enterprises, online reputation management of user-generated content, and incentive mechanisms for users’ online sharing behavior. In the future, it is expected that scholars will deepen and improve research in the field of tourism user-generated content in the Chinese context by probing the user-generated content of domestic tourists, employing management feedback strategies of domestic tourism enterprises, and exploring the user interaction modes of domestic homestay sharing platforms.

Scholars have focused increasingly on the use of mobile phone positioning data from base stations due to its spatiotemporal universality. In order to systematically analyze the research status of mobile phone positioning data from base stations and evaluate its application potential in the field of tourism, this article: (1) introduces the concept, classification, data characteristics, and research process of mobile phone positioning data from base stations; (2) identifies the five major categories of tourist identification and classification, tourism statistics, tourism flow analysis, tourist behavior research, and research on the impact of tourism activities; and (3) evaluates existing research from three dimensions, which are temporal scope, spatial scope, and research object/content. In addition, this study combines the research development of other fields in the use of mobile phone positioning data from base stations, discusses its weaknesses in the field of tourism, and concludes with suggestions for future research.

User-generated content (UGC) uploaded by tourists to social media platforms contains critical information about destination image. Studying the tourism destination images of local residents, domestic tourists, and foreign tourists can enrich and deepen the research on group differences of destination images while also providing a theoretical basis for tourism-related precision marketing. This paper uses a New York image metadata set on Flickr as its research materials. A total of 730,000 pictures from 2004 to 2014 are analyzed using computer text analysis, high-frequency word extraction, emotion polarity calculation, and other technologies to obtain the cognitive and emotional images of three different groups based on UGC image metadata. Research findings show that: (1) different groups have almost the same level of attention to the content of cognitive images; (2) there are discernible differences in the content of cognitive image construction by different groups, and the cognitive images of local residents are the richest; and (3) the overall evaluation of the affective images of different groups is positive, but some negative emotional tendency is also revealed. Finally, some practical suggestions are proposed based on research conclusions.

Although patriotic sentimentis an important research topic in social science research, the influence mechanism of tourism activities on patriotic sentiment has not yet been fully explored. Based on the data of tourists’ reviews of nine well-known domestic tourist destinations, this article constructs a tourism-related patriotism corpus. Using the tourism sentiment evaluation (TSE) model, it analyzes the content and characteristics of the influence of tourist destinations on the patriotic sentiment of Chinese tourists, thus illustrating the emotional expressions of Chinese tourists’ patriotic sentiment.
The study draws the following conclusions: (1) compared with general reviews, tourists’ reviews related to patriotic sentiment demonstrate strong positive sentiments, accounting for a much higher percentage of all reviews; (2) the resource structure and location of tourist destinations have a marked influence on domestic visitors’ sense of patriotic sentiment. Meanwhile, compared with natural attractions, artificial attractions involving historical culture and folklore may arouse tourists’ emotions of patriotic sentiment more considerably; and (3) a geographical location closely related to national and political factors can trigger stronger emotions of patriotic sentiment. The contribution of this research lies in the innovative use of big data of travelers’ reviews to capture Chinese tourists’ patriotic sentiment and in identifying the two most significant influencing factors of resource attributes and location. At the same time, it also provides new research methods for the study of patriotic sentiment. Finally, this study helps promote patriotic education and formulate better destination marketing strategies, thereby adding practical value and holding macro-strategic significance.