College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar.
Stud Health Technol Inform. 2022 Jun 29;295:209-212. doi: 10.3233/SHTI220699.
We studied the suitability of Artificial Intelligence (AI)-based models to predict vaccine-critical tweets on the social media platform Twitter. We manually labeled a sample of 800 tweets as either "vaccine-critical" (i.e, anti-vaccine tweets, mentioned concerns related to vaccine safety and efficacy, and are against vaccine mandates or vaccine passports) or "other" (i.e., tweets that are neutral, report news, or are ambiguous) and used them to train and test AI-based models for automatically predicting vaccine-critical tweets. We fine-tuned two pre-trained deep learning-based language models, BERT and BERTweet, and implemented four classical AI-based models, Random Forest, Logistics Regression, Linear Support Vector Machines, and Multinomial Naïve Bayes. We evaluated these AI-based models using f1 score, accuracy, precision, and recall in three-fold cross-validation. We found that BERTweet outperformed all other models using these measures.
我们研究了基于人工智能（AI）的模型在社交媒体平台 Twitter 上预测疫苗关键推文的适用性。我们手动标记了 800 条推文的样本，将其标记为“疫苗关键”（即反疫苗推文，提到与疫苗安全性和有效性相关的担忧，反对疫苗授权或疫苗护照）或“其他”（即中性推文、报道新闻或模棱两可的推文），并使用这些推文来训练和测试基于 AI 的模型，以自动预测疫苗关键推文。我们微调了两个预先训练的基于深度学习的语言模型，BERT 和 BERTweet，并实现了四个经典的基于 AI 的模型，随机森林、逻辑回归、线性支持向量机和多项式朴素贝叶斯。我们在三折交叉验证中使用 f1 分数、准确性、精度和召回率来评估这些基于 AI 的模型。我们发现，BERTweet 在这些指标上优于所有其他模型。
