Vaccine hesitancy has been a common concern, probably since vaccines were created and, with the popularisation of social media, people started to express their concerns about vaccines online alongside those posting pro- and anti-vaccine content. Predictably, since the first mentions of a COVID-19 vaccine, social media users posted about their fears and concerns or about their support and belief into the effectiveness of these rapidly developing vaccines. Identifying and understanding the reasons behind public hesitancy towards COVID-19 vaccines is important for policy markers that need to develop actions to better inform the population with the aim of increasing vaccine take-up. In the case of COVID-19, where the fast development of the vaccines was mirrored closely by growth in anti-vaxx disinformation, automatic means of detecting citizen attitudes towards vaccination became necessary. This is an important computational social sciences task that requires data analysis in order to gain in-depth understanding of the phenomena at hand. Annotated data is also necessary for training data-driven models for more nuanced analysis of attitudes towards vaccination. To this end, we created a new collection of over 3,101 tweets annotated with users' attitudes towards COVID-19 vaccination (stance). Besides, we also develop a domain-specific language model (VaxxBERT) that achieves the best predictive performance (73.0 accuracy and 69.3 F1-score) as compared to a robust set of baselines. To the best of our knowledge, these are the first dataset and model that model vaccine hesitancy as a category distinct from pro- and anti-vaccine stance.

翻译：疫苗犹豫自疫苗诞生以来可能一直是一个普遍关注的问题，随着社交媒体的普及，人们开始在网络上表达对疫苗的担忧，同时也有发布支持和反对疫苗内容的声音。可以预见的是，自COVID-19疫苗首次被提及以来，社交媒体用户纷纷发帖表达对快速研发中疫苗的恐惧和担忧，或对其有效性的支持和信念。识别并理解公众对COVID-19疫苗犹豫态度背后的原因，对于政策制定者至关重要——他们需要制定行动方案以更好地告知公众，从而提高疫苗接种率。在COVID-19的背景下，疫苗的快速研发与反疫苗虚假信息的同步增长，使得自动检测公民对疫苗接种态度的方法变得不可或缺。这是一项重要的计算社会科学任务，需要通过数据分析来深入理解当前现象。为了更细致地分析人们对疫苗接种的态度，训练数据驱动模型也需要标注数据。为此，我们创建了一个包含3101条标注了用户对COVID-19疫苗接种态度（立场）的推文新数据集。此外，我们还开发了一个领域特定语言模型（VaxxBERT），与一组强大的基线模型相比，该模型取得了最佳的预测性能（准确率73.0，F1分数69.3）。据我们所知，这是首个将疫苗犹豫作为独立于支持和反对疫苗立场的类别进行建模的数据集和模型。