Nudging Public Health Behaviors to Prevent COVID-19: A Systematic Review.

Many countries have implemented strict preventive measures and mandatory policies to curb virus transmission during the COVID-19 pandemic. Some have adopted softer approaches, such as nudge-based intervention, to influence public health behavior. This systematic review, conducted following Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) 2020 guidelines, aims to determine if the nudge-based intervention can effectively influence people's preventive behavior during the early period of the COVID-19 pandemic. The review indicated an overall positive outcome, but results were mixed as nudge-based interventions substantially depended on the situational context. While the review found that the nudging technique that presents and conveys decision-related information was essential to nudging people, a secondary nudge would often applied to deliver the interventions. In addition, there was no indication of an ideal nudge technique that would be effective in most situations. Conversely, our findings indicate that the nudge would likely suffer from habituation after repeated intervention or backfire due to inappropriate use of nudges. Also, the ceiling effect would inhibit any nudge influences regardless of the technique(s) used. In sum, the results and the applicability of nudge-based interventions were mixed, highlighting the need for further research to advance the theory and practical developments.

Examining COVID-19 Tweet Diffusion Using an Integrated Social Amplification of Risk and Issue-Attention Cycle Framework.

Drawing upon the social amplification of risk (SARF) and the issue-attention cycle framework, we examined the amplification of COVID-19 risk-related tweets through (a) topics: key interests of discussion; (b) temperament: emotions of tweets; (c) topography (i.e., location); and (d) temporality (i.e., over time). We computationally analyzed 1,641,273 tweets, and conducted manual content analysis on a subset of 6,000 tweets to identify how topics, temperament, and topography of COVID-19 tweets were associated with risk amplification - retweet and favorite count - using negative binomial regression. We found 11 dominant COVID-19 topics-health impact, economic impact, reports of lockdowns, report of new cases, the need to stay home, coping with COVID-19, news about President Trump, government support, fight with COVID-19 by non-government entities, origins, and preventive measure in our corpus of tweets across the issue-attention cycle. The negative binomial regression results showed that at the pre-problem stage, topics on President Trump, speculation of origins, and initiatives to fight COVID-19 by non-government entities were most likely to be amplified, underscoring the inherent politicization of COVID-19 and erosion of trust in governments from the start of the pandemic. We also found that while tweets with negative emotions were consistently amplified throughout the issue-attention cycle, surprisingly tweets with positive emotions were amplified during the height of the pandemic - this counter-intuitive finding indicated signs of premature and misplaced optimism. Finally, our results showed that the locations of COVID-19 tweet amplification corresponded to the shifting COVID-19 hotspots across different continents across the issue-attention cycle. Theoretical and practical implications of risk amplification on social media are discussed.

Leveraging MOOCs for learners in economically disadvantaged regions.

While Massive Online Open Courses (MOOCs) have seen a surge in enrollments in higher education around the world especially during the Covid-19 pandemic, it is unclear if learners from the economically disadvantaged regions (EDR) are also able to capitalize on them. Specifically, challenges related to using MOOCs in these regions have been reported in the literature. Thus, the objective of this paper is to address the pedagogical challenge by investigating approaches to leverage MOOCs for learners in EDR. Drawing from the ARCS (i.e. Attention, Relevance, Confidence and Satisfaction) model, we proposed an embedded MOOCs approach where bite-sized MOOCs segments are integrated into in-class lectures under the guidance of the instructors. The effectiveness of the embedded MOOCs approach was evaluated and compared with other instructional methods. Results from randomized experiments showed that the embedded MOOCs approach had higher evaluations in terms of attention, relevance and satisfaction than face-to-face learning approach. In addition, the embedded MOOCs approach outperformed asynchronously blended MOOCs in enhancing students' relevance perception. Regression analysis further revealed that attention, confidence, and satisfaction perceptions were positively associated with students' intention to adopt the embedded MOOCs approach in their future studies. The findings shed light on how to utilize MOOCs and re-use content in MOOCs for global benefits and enable new pedagogical developments. The findings also underscore the importance of local social support and offline interactions to support the online learning materials.

COVID-19 Tweet Links: A Preliminary Investigation of Type and Relevance.

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.

Understanding the effects of message cues on COVID-19 information sharing on Twitter.

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.

Understanding the Temporal Effects on Tweetcussion of COVID-19 Vaccine.

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.

Investigating Learner's Online Learning Behavioural Changes during the COVID-19 Pandemic.

The objective of this study is to understand the pandemic's impact on online learning behaviour based on learners' comments collected from online video tutorials hosted on YouTube. The topic modelling approach is employed to uncover the changes in topic prevalence during the pre- and post-pandemic timeframe to infer learning behaviour. Ten topics were uncovered, and each exhibited a varying degree of changes over time. Overall, the study identified two learning behavioural changes, (1) learners were more active in learning relevant skillsets through sharing their experiences; and (2) learners had altered their help-seeking behaviour to aid in their learning.