Background The COVID-19 outbreak fueled one of the most rapid vaccine developments in history. However, misinformation spread through online social media often leads to negative vaccine sentiment and hesitancy. Methods To investigate COVID-19 vaccine-related discussion in social media, we conducted a sentiment analysis and Latent Dirichlet Allocation topic modeling on textual data collected from 13 Reddit communities focusing on the COVID-19 vaccine from Dec 1, 2020, to May 15, 2021. Data were aggregated and analyzed by month to detect changes in any sentiment and latent topics. Results Polarity analysis suggested these communities expressed more positive sentiment than negative regarding the vaccine-related discussions and has remained static over time. Topic modeling revealed community members mainly focused on side effects rather than outlandish conspiracy theories. Conclusion Covid-19 vaccine-related content from 13 subreddits show that the sentiments expressed in these communities are overall more positive than negative and have not meaningfully changed since December 2020. Keywords indicating vaccine hesitancy were detected throughout the LDA topic modeling. Public sentiment and topic modeling analysis regarding vaccines could facilitate the implementation of appropriate messaging, digital interventions, and new policies to promote vaccine confidence.

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We used a pulsed laser ablation synthesis in solution (LASIS) to produce cobalt oxide/hydroxide nanoparticles (NPs) with tailored size, morphology and structure at different laser fluences, wavelengths (532 and 1064 nm) and solvent conditions. Specifically, LASIS on bulk Co in the presence and absence of O2 in an aqueous solution initially produces cobalt monoxide (CoO) and single crystal β-cobalt hydroxide (β-Co(OH)2) nanoparticles (NPs) respectively that finally transform into cobaltosic oxide (Co3O4) through oxidation and/or thermal decomposition. Transmission electron microscopy (TEM) and scanning mobility particle sizer (SMPS) measurements on the final products reveal a bimodal size distribution of agglomerated NPs (for the 1064 and 532 nm laser) at low laser fluences, where the ablation mechanism is dominated by vaporization and normal boiling. In contrast, more efficient and predominant explosive boiling at higher laser fluences produces a mono-modal size distribution of spherically shaped primary NPs in agglomerates. Furthermore, higher absorbance of the 532 nm laser by solution-phase colloidal NPs re-ablates them into spherical shapes of larger size (∼13-22 nm) as compared to the ones from using 1064 nm LASIS (∼10-14 nm), while rendering 532 nm LASIS less productive than 1064 nm LASIS over an extended period of time. Finally, Co3O4 nanorods with enhanced localized surface plasmon resonance (LSPR) are synthesized at high pH (pH ≥ 13) and low laser fluence (<5 mJ cm(-2)) conditions. Such nanostructured materials are promising candidates as photocatalysts or additives in nanocomposite materials with enhanced light absorption properties.

Background The emergence of the novel coronavirus (COVID-19) and the necessary separation of populations have led to an unprecedented number of new social media users seeking information related to the pandemic. Currently, with an estimated 4.5 billion users worldwide, social media data offer an opportunity for near real-time analysis of large bodies of text related to disease outbreaks and vaccination. These analyses can be used by officials to develop appropriate public health messaging, digital interventions, educational materials, and policies. Objective Our study investigated and compared public sentiment related to COVID-19 vaccines expressed on 2 popular social media platforms—Reddit and Twitter—harvested from January 1, 2020, to March 1, 2022. Methods To accomplish this task, we created a fine-tuned DistilRoBERTa model to predict the sentiments of approximately 9.5 million tweets and 70 thousand Reddit comments. To fine-tune our model, our team manually labeled the sentiment of 3600 tweets and then augmented our data set through back-translation. Text sentiment for each social media platform was then classified with our fine-tuned model using Python programming language and the Hugging Face sentiment analysis pipeline. Results Our results determined that the average sentiment expressed on Twitter was more negative (5,215,830/9,518,270, 54.8%) than positive, and the sentiment expressed on Reddit was more positive (42,316/67,962, 62.3%) than negative. Although the average sentiment was found to vary between these social media platforms, both platforms displayed similar behavior related to the sentiment shared at key vaccine-related developments during the pandemic. Conclusions Considering this similar trend in shared sentiment demonstrated across social media platforms, Twitter and Reddit continue to be valuable data sources that public health officials can use to strengthen vaccine confidence and combat misinformation. As the spread of misinformation poses a range of psychological and psychosocial risks (anxiety and fear, etc), there is an urgency in understanding the public perspective and attitude toward shared falsities. Comprehensive educational delivery systems tailored to a population’s expressed sentiments that facilitate digital literacy, health information–seeking behavior, and precision health promotion could aid in clarifying such misinformation.

Almost half of the world population has received at least one dose of vaccine against the COVID-19 virus. However, vaccine hesitancy amongst certain populations is driving new waves of infections at alarming rates. The popularity of online social media platforms attracts supporters of the anti-vaccination movement who spread misinformation about vaccine safety and effectiveness. We conducted a semantic network analysis to explore and analyze COVID-19 vaccine misinformation on the Reddit social media platform.

The COVID-19 pandemic has profoundly impacted the world, having taken the lives of over 6 million individuals. Accordingly, this pandemic has caused a shift in conversations surrounding the burden of diseases worldwide, welcoming insights from multidisciplinary fields including digital health and artificial intelligence. Africa faces a heavy disease burden that exacerbates the current COVID-19 pandemic and limits the scope of public health preparedness, response, containment, and case management. Herein, we examined the potential impact of transformative digital health technologies in mitigating the global health crisis with reference to African countries. Furthermore, we proposed recommendations for scaling up digital health technologies and artificial intelligence–based platforms to tackle the transmission of the SARS-CoV-2 and enable equitable vaccine access. Challenges related to the pandemic are numerous. Rapid response and management strategies—that is, contract tracing, case surveillance, diagnostic testing intensity, and most recently vaccine distribution mapping—can overwhelm the health care delivery system that is fragile. Although challenges are vast, digital health technologies can play an essential role in achieving sustainable resilient recovery and building back better. It is plausible that African nations are better equipped to rapidly identify, diagnose, and manage infected individuals for COVID-19, other diseases, future outbreaks, and pandemics.

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Objectives The COVID-19 pandemic has introduced new opportunities for health communication, including an increase in the public’s use of online outlets for health-related emotions. People have turned to social media networks to share sentiments related to the impacts of the COVID-19 pandemic. In this paper, we examine the role of social messaging shared by Persons in the Public Eye (ie, athletes, politicians, news personnel, etc) in determining overall public discourse direction. Methods We harvested approximately 13 million tweets ranging from 1 January 2020 to 1 March 2022. The sentiment was calculated for each tweet using a fine-tuned DistilRoBERTa model, which was used to compare COVID-19 vaccine-related Twitter posts (tweets) that co-occurred with mentions of People in the Public Eye. Results Our findings suggest the presence of consistent patterns of emotional content co-occurring with messaging shared by Persons in the Public Eye for the first 2 years of the COVID-19 pandemic influenced public opinion and largely stimulated online public discourse. Discussion We demonstrate that as the pandemic progressed, public sentiment shared on social networks was shaped by risk perceptions, political ideologies and health-protective behaviours shared by Persons in the Public Eye, often in a negative light. Conclusion We argue that further analysis of public response to various emotions shared by Persons in the Public Eye could provide insight into the role of social media shared sentiment in disease prevention, control and containment for COVID-19 and in response to future disease outbreaks.