Public sentiment analysis and topic modeling regarding COVID-19 vaccines on the Reddit social media platform: A call to action for strengthening vaccine confidence

Abstract

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.

Spatial evolution patterns of public panic on Chinese social networks amidst the COVID-19 pandemic

“…Analysing social media data with users' geo-mapped opinions and sentiments can help obtain a holistic view of the country's general mood regarding a pandemic and understand the distribution and patterns affected by this crisis [ 12 , 13 ]. Previous studies on social media sentiment analysis during the COVID-19 pandemic have focused on the percentage and distribution of positive, negative, and neutral sentiments in large-scale tweets [ 14 ], and machine learning techniques for sentiment identification and text analysis of particular topics such as vaccines [ 15 ] and prevention attitudes [ 16 ]. However, mainstream machine learning methods are not sufficiently accurate for fine-grained sentiment analysis.…”

Network Analysis of COVID-19 Vaccine Misinformation on Social Media

“…Because the causes of vaccine hesitancy are multifaceted, digital intervention technologies should be adopted to surveil and recognize the type of hesitant behavior so an appropriate response can be implemented. These results accompany ongoing research focused on detecting and categorizing vaccine misinformation in social media and other public forums [5]. The information gained from network analysis could be especially useful in designing custom digital technologies [6,7] to educate vaccine-hesitant users or combat outright dangerous misinformation designed to dissuade users from being vaccinated.…”

Misinformation about Covid-19 Vaccines on Social Media: Rapid Review

“…Two studies examined English language messages or discourse on Reddit. 60,63 One study looked at anti-vaccine discussions on both Reddit and the Polish social media platform Interia. 64 Two studies examined YouTube videos in English 44 and in Spanish.…”

“…The studies with low risk of bias that specified the social media platform they were assessing focused on Twitter only 35 and several platforms, such as Facebook, Instagram, YouTube, WhatsApp, WeChat, Weibo, and TikTok. 30,34,36,37 Among the studies that collected data from social media (Table 2), ten studies extracted data from Twitter, 45,48,49,51,53,54,[57][58][59]65 two from YouTube, 44,52 one from TikTok, 47 one from Reddit, 60 one from Parler, 46 and one from Facebook. 66 Seven studies that collected data from social media utilized two or more platforms.…”