ABSTRACT
Analyzing and documenting human information behaviors in the context of global public health crises such as the COVID-19 pandemic are critical to informing crisis management. Drawing on the Elaboration Likelihood Model, this study investigates how three types of peripheral cues-content richness, emotional valence, and communication topic-are associated with COVID-19 information sharing on Twitter. We used computational methods, combining Latent Dirichlet Allocation topic modeling with psycholinguistic indicators obtained from the Linguistic Inquiry and Word Count dictionary to measure these concepts and built a research model to assess their effects on information sharing. Results showed that content richness was negatively associated with information sharing. Tweets with negative emotions received more user engagement, whereas tweets with positive emotions were less likely to be disseminated. Further, tweets mentioning advisories tended to receive more retweets than those mentioning support and news updates. More importantly, emotional valence moderated the relationship between communication topics and information sharing-tweets discussing news updates and support conveying positive sentiments led to more information sharing; tweets mentioning the impact of COVID-19 with negative emotions triggered more sharing. Finally, theoretical and practical implications of this study are discussed in the context of global public health communication.
ABSTRACT
We conducted an exploratory study of the links found in Twitter tweets. Our results showed that the largest category of tweet links was social media platforms followed by alternative news sites. Government agencies and educational institutions were under-represented. In terms of relevance, about 75% of the links were related to COVID-19 but disappointingly, only 40% of the links were directly related to their respective tweets' topics.
ABSTRACT
In the fight against COVID-19, the Pfizer and BioNTech vaccine announcement marked a significant turning point. Analysing the topics discussed surrounding the announcement is critical to shed light on how people respond to the vaccination against COVID-19. Specifically, since the COVID-19 vaccine was developed at unprecedented speed, different segments of the public with a different understanding of the issues may react and respond differently. We analysed Twitter tweets to uncover the issues surrounding people's discussion of the vaccination against COVID-19. Through the use of Latent Dirichlet Allocation (LDA), nine topics were identified pertaining to vaccine-related tweets. We analysed the temporal differences in the nine topics, prior and after the official vaccine announcement.
ABSTRACT
The current study systematically reviews and summarizes the existing literature of game acceptance, identifies the core determinants, and evaluates the strength of the relationships in the extended technology acceptance model. Moreover, this study segments video games into two categories: hedonic and utilitarian and examines player acceptance of these two types separately. Through a meta-analysis of 50 articles, we find that perceived ease of use (PEOU), perceived usefulness (PU), and perceived enjoyment (PE) significantly associate with attitude and behavioral intention. PE is the dominant predictor of hedonic game acceptance, while PEOU and PU are the main determinants of utilitarian game acceptance. Furthermore, we find that respondent type and game platform are significant moderators. Findings of this study provide critical insights into the phenomenon of game acceptance and suggest directions for future research.
