In the big data era, destination images have played an increasingly important role in tourism development. However, seldom tourism research has utilised big data analytics to examine destination images from travel blogs. Therefore, this study proposes and evaluates a big data analytical approach using latent Dirichlet allocation to extract attributes of online destination images from 140,286 travel blogs about 20 cities in China. Results reveal 14 dimensions with 54 attributes of destination images of the studied cities. Interesting findings are discovered between online destination images and tourism cities. This study also summarises the implications for tourism research and practice.

Sentiment analytics, as a computational method to extract emotion and detect polarity, has gained increasing attention in tourism research. However, issues regarding how to properly apply sentiment analytics are seldom addressed in the tourism literature. This study addresses such methodological challenges by employing the metalearning perspective to examine the design effects on predictive accuracy using a sentiment analysis experiment for Chinese travel news. Our results reveal strong interactions among key design factors of sentiment analytics on predictive accuracy; accordingly, this study formulates a metalearning framework to improve predictive accuracy for computational tourism research. Our study attempts to highlight and improve the methodological relevance and appropriateness of sentiment analytics for future tourism studies.

The progress in sentiment analytics and communication research provides a powerful scaffold by which to reexamine the long-debated research on residents’ attitudes toward tourism. To mitigate the limitations of the classical survey-based research method, this study takes a news media sentiment analytics perspective to unveil how the residents’ attitudes toward tourism evolve over time and how socioeconomic factors interact with such evolving attitudes in the context of Hong Kong. Drawn on a news data set containing 72,755 news articles published in Chinese language newspapers, this study computes the overall news sentiments for 156 calendar months since 2003, examines the face validity and nomological validity of the results, and discusses the long-run dynamics between residents’ attitudes and typical socioeconomic factors. This study adds a vital dimension to current residents’ attitudes research and practices from data-scarce to data-rich studies and from static snapshots to dynamic unfolding.

Emotional reactions and transmissions are crucial to host-tourist interaction yet lacking in research, particularly from the host viewpoint. To deepen understanding of host-tourist interaction, this study took a host perspective to examine emotional contagion from tourists to hosts. By adopting video-vignette based interaction scenarios and cutting-edge techniques (e.g., FaceReader), a real-time multi-modal investigation was undertaken to reveal mechanism underlying emotional contagion of Hong Kong residents from Mainland Chinese tourists. Results theoretically consolidated the dual-process mechanism underpinning automatic emotional contagion and empirically verified an Emotional Contagion Model (ECM) from tourists to hosts. The compelling effects of mimicry, interaction context and stereotypes explained the emotional convergence and divergence between hosts and tourists. The study extended the knowledge boundary of host-tourist interaction to micro-level interpersonal emotional connection. Moreover, the verified ECM theoretically advances emotional contagion mechanism in the social psychology literature. Practical guidelines for host-tourist relation management and sustainable destination development were provided.

The intricate relationships between tourists’ value co-creation behaviors and value perception in the sharing economy have attracted increasing attention from researchers and practitioners. Unlike previous studies that focused on such relationships at a particular time point, this study sheds light on the dynamics of tourists’ value co-creation behaviors and employs social media analytics to reveal their longitudinal role in value perception. Our empirical specifications reveal the long-run one-way causality of such relationships, disclose their inverted U-shaped relationships over time, and quantify the boundary thresholds at which value co-creation transforms into value co-destruction. These findings also uncover a significant interaction effect across two value co-creation behavior dimensions, that is, participation behaviors and citizenship behaviors. This study enriches our knowledge of the duality of tourists’ value co-creation behaviors, and offers insights into how to leverage the boundary thresholds of value co-creation behaviors to formulate customized tourism marketing strategies in the sharing economy.