COVID-19 vaccine hesitancy: a social media analysis using deep learning

Abstract

Hesitant attitudes have been a significant issue since the development of the first vaccines-the WHO sees them as one of the most critical global health threats. The increasing use of social media to spread questionable information about vaccination strongly impacts the population's decision to get vaccinated. Developing text classification methods that can identify hesitant messages on social media could be useful for health campaigns in their efforts to address negative influences from social media platforms and provide reliable information to support their strategies against hesitant-vaccination sentiments. This study aims to evaluate the performance of different machine learning models and deep learning methods in identifying vaccine-hesitant tweets that are being published during the COVID-19 pandemic. Our concluding remarks are that Long Short-Term Memory and Recurrent Neural Network models have outperformed traditional machine learning models on detecting vaccine-hesitant messages in social media, with an accuracy rate of 86% against 83%.

Evaluating the impact of a linguistically and culturally tailored social media ad campaign on COVID-19 vaccine uptake among indigenous populations in Guatemala: a pre/post design intervention study

“…Like other studies, our findings show that social media offers a unique opportunity to improve health communications targeted at hard-to-reach populations 26–29. In addition to providing educational information, these campaigns help counter the increasing amount of misinformation on these platforms.…”

“…Like other studies, our findings show that social media offers a unique opportunity to improve health communications targeted at hard-to-reach populations. [26][27][28][29] In addition to providing educational information, these campaigns help counter the increasing amount of misinformation on these platforms. Our study adds to the limited existing evidence on real world impacts of social media campaigns on behaviours such as vaccine uptake.…”

COVID-19 Vaccine Hesitancy in China: An Analysis of Reasons through Mixed Methods

“…Since the COVID-19 outbreak, many Chinese have used social media to exchange information about the outbreak and vaccine, making Weibo one of the main venues for Chinese internet users to obtain and discuss health information [ 15 , 16 , 17 ]. Some studies confirm that social media does provide space for the public to express vaccine hesitations, which makes Weibo a mixed space containing public vaccine views and emotions [ 18 ]. Many active Weibo users and the massive amount of vaccine discussions also provide a sample source for our study of the causes of COVID-19 vaccine hesitancy in the Chinese population [ 19 , 20 ].…”

The role of cryptocurrencies in predicting oil prices pre and during COVID-19 pandemic using machine learning

“…First, we use machine learning (Karim et al, 2021;Kazancoglu et al, 2022;Khalilpourazari & Hashemi Doulabi, 2022) to understand the predictability power of cryptocurrencies, the US dollar, and the COVID-19 on oil prices. Other studies used conventional models to investigate this relationship (e.g., Albulescu & Ajmi, 2021;Bénassy-Quéré et al, 2007;Charfeddine et al, 2020;Jareño et al, 2021;Mensi et al, 2020;Okorie & Lin, 2020;Kumar et al, 2022aKumar et al, , 2022bKumar et al, 2022aKumar et al, , 2022bNyawa et al, 2022;Queiroz et al, 2020;Queiroz & Fosso Wamba, 2021;Wen et al, 2018;Zhang et al, 2008). Second, we cover a longer period during COVID-19.…”