Social media study of public opinions on potential COVID-19 vaccines: informing dissent, disparities, and dissemination

Background The current development of vaccines for severe acute respiratory syndrome coronavirus 2(SARSCoV-2)is unprecedented.Little is known,however,about the nuanced public opinions on the vaccines on social media.Methods We adopted a human-guided machine learning framework using more than six million tweets from almost two million unique Twitter users to capture public opinions on the vaccines for SARS-CoV-2,classifying them into three groups:pro-vaccine,vaccine-hesitant,and anti-vaccine.After feature inference and opinion mining,10,945 unique Twitter users were included in the study population.Multinomial logistic regression and counterfactual analysis were conducted.Results Socioeconomically disadvantaged groups were more likely to hold polarized opinions on coronavirus disease 2019(COVID-19)vaccines,either pro-vaccine(B=0.40,SE=0.08,P<0.001,OR=1.49;95%CI=1.26-1.75)or anti-vaccine(B=0.52,SE=0.06,P<0.001,OR=1.69;95%CI=1.49-1.91).People who have the worst personal pandemic experience were more likely to hold the anti-vaccine opinion(B=−0.18,SE=0.04,P<0.001,OR=0.84;95%CI=0.77-0.90).The United States public is most concerned about the safety,effectiveness,and political issues regarding vaccines for COVID-19,and improving personal pandemic experience increases the vaccine acceptance level.

Misinformation versus Facts:Understanding the Influence of News regarding COVID-19 Vaccines on Vaccine Uptake

Background. There is a lot of fact-based information and misinformation in the online discourses and discussions about theCOVID-19 vaccines. Method. Using a sample of nearly four million geotagged English tweets and the data from the CDCCOVID Data Tracker, we conducted the Fama-MacBeth regression with the Newey-West adjustment to understand theinfluence of both misinformation and fact-based news on Twitter on the COVID-19 vaccine uptake in the US from April 19when US adults were vaccine eligible to June 30, 2021, after controlling state-level factors such as demographics, education,and the pandemic severity. We identified the tweets related to either misinformation or fact-based news by analyzing theURLs. Results. One percent increase in fact-related Twitter users is associated with an approximately 0.87 decrease (B = −0:87,SE = 0:25, and p < :001) in the number of daily new vaccinated people per hundred. No significant relationship was foundbetween the percentage of fake-news-related users and the vaccination rate. Conclusion. The negative association between thepercentage of fact-related users and the vaccination rate might be due to a combination of a larger user-level influence and thenegative impact of online social endorsement on vaccination intent.

What Contributes to a Crowdfunding Campaign’s Success?Evidence and Analyses from GoFundMe Data

Researchers have attempted to measure the success of crowdfunding campaigns using a variety of determinants,such as the descriptions of the crowdfunding campaigns,the amount of funding goals,and crowdfunding project characteristics.Although many successful determinants have been reported in the literature,it remains unclear whether the cover photo and the text in the title and description could be combined in a fusion classifier to better predict the crowdfunding campaign’s success.In this work,we focus on the performance of the crowdfunding campaigns on GoFundMe across a wide variety of funding categories.We analyze the attributes available at the launch of the campaign and identify attributes that are important for each category of the campaigns.Furthermore,we develop a fusion classifier based on the random forest that significantly improves the prediction result,thus suggesting effective ways to make a campaign successful.

The COVID-19 Pandemic and Mental Health Concerns on Twitter in the United States

Background.During the COVID-19 pandemic,mental health concerns(such as fear and loneliness)have been actively discussed on social media.We aim to examine mental health discussions on Twitter during the COVID-19 pandemic in the US and infer the demographic composition of Twitter users who had mental health concerns.Methods.COVID-19-related tweets from March 5th,2020,to January 31st,2021,were collected through Twitter streaming API using keywords(i.e.,“corona,”“covid19,”and“covid”).By further filtering using keywords(i.e.,“depress,”“failure,”and“hopeless”),we extracted mental health-related tweets from the US.Topic modeling using the Latent Dirichlet Allocation model was conducted to monitor users’discussions surrounding mental health concerns.Deep learning algorithms were performed to infer the demographic composition of Twitter users who had mental health concerns during the pandemic.Results.We observed a positive correlation between mental health concerns on Twitter and the COVID-19 pandemic in the US.Topic modeling showed that“stay-at-home,”“death poll,”and“politics and policy”were the most popular topics in COVID-19 mental health tweets.Among Twitter users who had mental health concerns during the pandemic,Males,White,and 30-49 age group people were more likely to express mental health concerns.In addition,Twitter users from the east and west coast had more mental health concerns.Conclusions.The COVID-19 pandemic has a significant impact on mental health concerns on Twitter in the US.Certain groups of people(such as Males and White)were more likely to have mental health concerns during the COVID-19 pandemic.

Characterizing Discourse about COVID-19 Vaccines:A Reddit Version of the Pandemic Story

It has been one year since the outbreak of the COVID-19 pandemic.The good news is that vaccines developed by several manufacturers are being actively distributed worldwide.However,as more and more vaccines become available to the public,various concerns related to vaccines become the primary barriers that may hinder the public from getting vaccinated.Considering the complexities of these concerns and their potential hazards,this study is aimed at offering a clear understanding about different population groups’underlying concerns when they talk about COVID-19 vaccines—particularly those active on Reddit.The goal is achieved by applying LDA and LIWC to characterize the pertaining discourse with insights generated through a combination of quantitative and qualitative comparisons.Findings include the following:(1)during the pandemic,the proportion of Reddit comments predominated by conspiracy theories outweighed that of any other topics;(2)each subreddit has its own user bases,so information posted in one subreddit may not reach that from other subreddits;and(3)since users’concerns vary across time and subreddits,communication strategies must be adjusted according to specific needs.The results of this study manifest challenges as well as opportunities in the process of designing effective communication and immunization programs.