Nyawa Serge, Tchuente Dieudonné, Fosso-Wamba Samuel
Department of Information, Operations and Management Sciences, TBS Business School, 1 Place Alphonse Jourdain, 31068 Toulouse, France.
Ann Oper Res. 2022 Jun 16:1-39. doi: 10.1007/s10479-022-04792-3.
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%.
自首批疫苗研发以来，犹豫态度一直是一个重大问题——世界卫生组织将其视为最严重的全球健康威胁之一。社交媒体越来越多地被用于传播有关疫苗接种的可疑信息，这对人们的接种决定产生了强烈影响。开发能够识别社交媒体上犹豫信息的文本分类方法，对于健康宣传活动应对社交媒体平台的负面影响、提供可靠信息以支持其抵制疫苗犹豫情绪的策略可能会有所帮助。本研究旨在评估不同机器学习模型和深度学习方法在识别新冠疫情期间发布的疫苗犹豫推文方面的性能。我们的结论是，长短期记忆模型和循环神经网络模型在检测社交媒体上的疫苗犹豫信息方面优于传统机器学习模型，准确率分别为86%和83%。
