Purpose: The purpose of this paper is to explore the research status and development trend of the field of event detection in social media (ED in SM) through a bibliometric analysis of academic publications. Design/methodology/approach: First, publication distributions are analyzed including the trends of publications and citations, subject distribution, predominant journals, affiliations, authors, etc. Second, an indicator of collaboration degree is used to measure scientific connective relations from different perspectives. A network analysis method is then applied to reveal scientific collaboration relations. Furthermore, based on keyword co-occurrence analysis, major research themes and their evolutions throughout time span are discovered. Finally, a network analysis method is applied to visualize the analysis results. Findings: The area of ED in SM has received increasing attention and interest in academia with Computer Science and Engineering as two major research subjects. The USA and China contribute the most to the area development. Affiliations and authors tend to collaborate more with those within the same country. Among the 14 identified research themes, newly emerged themes such as Pharmacovigilance event detection are discovered. Originality/value: This study is the first to comprehensively illustrate the research status of ED in SM by conducting a bibliometric analysis. Up-to-date findings are reported, which can help relevant researchers understand the research trend, seek scientific collaborators and optimize research topic choices.

Red tourism, as a form of special interest tourism (SIT), becomes widespread among Chinese tourists. This study aims to explore memorable tourism experiences (MTEs) in red tourism destinations and examines how country competence affects intention to visit similar destinations through the influences on MTEs, destination image, red tourism place attachment, and overall satisfaction. The partial least squares structural equation modeling (PLS-SEM) analysis is utilized to analyze the data from 556 tourists. Empirical results reveal that country competence positively influences MTEs and destination image. Furthermore, both MTEs and destination image influence overall satisfaction and red tourism place attachment, but do not influence intention to visit other similar destinations; the relationships between overall satisfaction, intention to visit other similar destinations, and place attachment are all confirmed as well. This study represents one of the few studies that classify red tourism as a form of historical-based SIT, with the purpose of promoting country competence. The outcomes provide a better understanding of both scholars and practitioners of historical-based SIT planning and businesses on how to promote destination image and country competence.

Natural Language Processing (NLP) empowered mobile computing is the use of NLP techniques in the context of mobile environment. Research in this field has drawn much attention given the continually increasing number of publications in the last five years. This study presents the status and development trend of the research field through an objective, systematic, and comprehensive review of relevant publications available from Web of Science. Analysis techniques including a descriptive statistics method, a geographic visualization method, a social network analysis method, a latent dirichlet allocation method, and an affinity propagation clustering method are used. We quantitatively analyze the publications in terms of statistical characteristics, geographical distribution, cooperation relationship, and topic discovery and distribution. This systematic analysis of the field illustrates the publications evolution over time and identifies current research interests and potential directions for future research. Our work can potentially assist researchers in keeping abreast of the research status. It can also help monitoring new scientific and technological development in the research field.

This study introduces a new travel pattern “pandemic-restricted travel” that exists from COVID-19 based on prospect theory. The purpose of this study is to incorporate the motivation to travel and constraint to normal travel to predict tourists’ intention to continue visiting other alternative destinations due to COVID-19 restrictions. This study first generated the items of motivation to travel and constraints to normal travel from a focus group interview with 15 travel industry professionals in December 2020 in Zhuhai. Then, an online survey collected data from 416 respondents in the Greater Bay Area of China from January to February 2021. The results of exploratory factor analysis using SPSS identified two factors of motivation to travel (leisure and exploration) and two factors of favourable constraints to normal travel (policy restriction and perceived risk). The results of partial least squares–structural equation modelling (PLS-SEM) indicated that these four factors positively influence satisfaction but only leisure and exploration factors positively influence the intention of continuous pandemic-restricted travel. Among the four factors, leisure has the strongest impact on both satisfaction and intention of continue travelling. The results also revealed that satisfaction fully mediates the effects of two constraint factors and partially mediates the effects of two motivation factors on the intention of continuous pandemic-restricted travel. Implications for researchers and governments for pandemic-restricted travel during and in the post-COVID-19 era are then discussed.