Although billions of COVID-19 vaccines have been administered, too many people remain hesitant. Misinformation about the COVID-19 vaccines, propagating on social media, is believed to drive hesitancy towards vaccination. However, exposure to misinformation does not necessarily indicate misinformation adoption. In this paper we describe a novel framework for identifying the stance towards misinformation, relying on attitude consistency and its properties. The interactions between attitude consistency, adoption or rejection of misinformation and the content of microblogs are exploited in a novel neural architecture, where the stance towards misinformation is organized in a knowledge graph. This new neural framework is enabling the identification of stance towards misinformation about COVID-19 vaccines with state-of-the-art results. The experiments are performed on a new dataset of misinformation towards COVID-19 vaccines, called CoVaxLies, collected from recent Twitter discourse. Because CoVaxLies provides a taxonomy of the misinformation about COVID-19 vaccines, we are able to show which type of misinformation is mostly adopted and which is mostly rejected.
翻译:虽然已经使用数十亿的COVID-19疫苗,但许多人仍然犹豫不决。关于COVID-19疫苗的错误信息,在社交媒体上传播,据信会引发对疫苗接种的犹豫不决;然而,暴露错误信息并不一定表明采用了错误信息;在本文中,我们描述了一个新框架,用以根据态度的一致性及其特性,确定对错误信息的立场;在新型神经结构中利用了态度一致性、采纳或拒绝错误信息与微博客内容之间的相互作用,在这个结构中,对错误信息的立场在知识图中组织起来。这个新的神经框架有助于识别对具有最新结果的COVID-19疫苗错误信息的立场。这些实验是在最新的Twitter讨论中收集的关于COVID-19疫苗(称为CoVaxLies)错误信息的新数据集上进行的。由于CoVaxLies提供了有关CVID-19疫苗的错误信息的分类,我们可以显示哪些类型的错误信息大多被采纳,而大部分被否决。