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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. [ABSTRACT FROM AUTHOR]

Deep learning has garnered increasing attention in many research fields. However, prior research seldom focused on tourists' perception prediction and prescription towards tourism destinations in turbulence. This study attempts to fill the gap by investigating Mainland Chinese tourists' perception of a turbulent Hong Kong society through deep learning‐based sentiment analytics. This incorporates a convolutional neural network (CNN) model for sentiment prediction and feature frequency analysis for sentiment prescription for 52,950 Chinese travel microblogs about Hong Kong. Results show that the CNN‐based deep learning approach can obtain improved sentiment predictive performance with minimal domain knowledge and human effort. The trend of Mainland Chinese tourists' (MCTs') sentiments about Hong Kong and the unique sentiment features revealed by our approach can provide practitioners with new insights into design customised tourism marketing and development strategies. The MCTs' shared mentalities emerged from sentiment features may help to enhance our theoretical knowledge about tourists' perception in turbulent tourism markets. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]