Previous studies have highlighted the importance of vaccination as an effective strategy to control the transmission of the COVID-19 virus. It is crucial for policymakers to have a comprehensive understanding of the public's stance towards vaccination on a large scale. However, attitudes towards COVID-19 vaccination, such as pro-vaccine or vaccine hesitancy, have evolved over time on social media. Thus, it is necessary to account for possible temporal shifts when analysing these stances. This study aims to examine the impact of temporal concept drift on stance detection towards COVID-19 vaccination on Twitter. To this end, we evaluate a range of transformer-based models using chronological and random splits of social media data. Our findings demonstrate significant discrepancies in model performance when comparing random and chronological splits across all monolingual and multilingual datasets. Chronological splits significantly reduce the accuracy of stance classification. Therefore, real-world stance detection approaches need to be further refined to incorporate temporal factors as a key consideration.

翻译：既往研究强调了疫苗接种作为控制新冠病毒传播有效策略的重要性。决策者需要全面了解公众对疫苗接种的广泛立场。然而，社交媒体上关于COVID-19疫苗接种的态度（如支持接种或接种犹豫）随时间不断演变。因此，在分析这些立场时必须考虑潜在的时间偏移。本研究旨在探讨时间概念漂移对Twitter上COVID-19疫苗接种立场检测的影响。为此，我们采用时间顺序随机分割与完全随机分割的社交媒体数据，评估了一系列基于Transformer的模型。研究结果表明，在单语与多语言数据集中，时间顺序分割与随机分割下的模型性能存在显著差异。时间顺序分割显著降低了立场分类的准确率。因此，现实场景下的立场检测方法需进一步优化，将时间因素作为关键考量纳入分析框架。