Anticipating Z-generation tourists' green hotel visit intention utilizing an extended theory of planned behavior.

Due to the effect COVID-19 epidemic, promoting green consumption is now a key marketing strategy in the hospitality and tourism industry. As it is vital green hotels predict their customers' visit intention, this study attempts to discover the factors affecting Taiwan's Z-generation tourists' green hotel visit intention using an extended theory of planned behavior [including personal moral norms (PMN) and environmental concern (EC)]. Data were gathered from 296 Z-generation tourists via an online survey, which was subsequently analyzed using partial least squares structural equation modeling. The results evidence that Z-generation tourists' attitude, subjective norms, (SN) and perceived behavioral control positively and significantly influence their green hotel visit intention, with attitude being the most significant factor. Moreover, the mediation model analysis indicates Z-generation tourists' attitude toward green hotels mediates the relationships between PMN, SN, EC, and visit intention. This study provides new insights into tourists' green hotel visit intention and emphasizes the importance of attitude in the formation of intention.

Visual Persuasion in COVID-19 Social Media Content: A Multi-Modal Characterization (preprint)

Social media content routinely incorporates multi-modal design to covey information and shape meanings, and sway interpretations toward desirable implications, but the choices and outcomes of using both texts and visual images have not been sufficiently studied. This work proposes a computational approach to analyze the outcome of persuasive information in multi-modal content, focusing on two aspects, popularity and reliability, in COVID-19-related news articles shared on Twitter. The two aspects are intertwined in the spread of misinformation: for example, an unreliable article that aims to misinform has to attain some popularity. This work has several contributions. First, we propose a multi-modal (image and text) approach to effectively identify popularity and reliability of information sources simultaneously. Second, we identify textual and visual elements that are predictive to information popularity and reliability. Third, by modeling cross-modal relations and similarity, we are able to uncover how unreliable articles construct multi-modal meaning in a distorted, biased fashion. Our work demonstrates how to use multi-modal analysis for understanding influential content and has implications to social media literacy and engagement.

Characterizing User Susceptibility to COVID-19 Misinformation on Twitter (preprint)

Though significant efforts such as removing false claims and promoting reliable sources have been increased to combat COVID-19 "misinfodemic", it remains an unsolved societal challenge if lacking a proper understanding of susceptible online users, i.e., those who are likely to be attracted by, believe and spread misinformation. This study attempts to answer {\it who} constitutes the population vulnerable to the online misinformation in the pandemic, and what are the robust features and short-term behavior signals that distinguish susceptible users from others. Using a 6-month longitudinal user panel on Twitter collected from a geopolitically diverse network-stratified samples in the US, we distinguish different types of users, ranging from social bots to humans with various level of engagement with COVID-related misinformation. We then identify users' online features and situational predictors that correlate with their susceptibility to COVID-19 misinformation. This work brings unique contributions: First, contrary to the prior studies on bot influence, our analysis shows that social bots' contribution to misinformation sharing was surprisingly low, and human-like users' misinformation behaviors exhibit heterogeneity and temporal variability. While the sharing of misinformation was highly concentrated, the risk of occasionally sharing misinformation for average users remained alarmingly high. Second, our findings highlight the political sensitivity activeness and responsiveness to emotionally-charged content among susceptible users. Third, we demonstrate a feasible solution to efficiently predict users' transient susceptibility solely based on their short-term news consumption and exposure from their networks. Our work has an implication in designing effective intervention mechanism to mitigate the misinformation dissipation.