论文标题
在Twitter话语中确定针对Covid-19疫苗的采用或拒绝错误信息
Identifying the Adoption or Rejection of Misinformation Targeting COVID-19 Vaccines in Twitter Discourse
论文作者
论文摘要
尽管已经施用了数十亿次Covid-19-19疫苗，但太多的人仍然犹豫。据信，关于在社交媒体上传播的Covid-19疫苗的错误信息被认为推动了犹豫疫苗接种。但是，暴露于错误信息并不一定表明采用了错误信息。在本文中，我们描述了一个新颖的框架，以依靠态度一致性及其特性来确定对错误信息的立场。态度一致性，采用或拒绝错误信息与微博的内容之间的相互作用是在一种新颖的神经结构中利用的，在新的神经结构中，在知识图中组织了错误的信息。这个新的神经框架使人们能够鉴定出对Covid-19-19疫苗的错误信息，并具有最先进的结果。这些实验是从最近从最近的Twitter话语中收集的新的Covid-19疫苗的新数据集（称为Covaxlies）进行的。由于Covaxlies提供了关于Covid-19疫苗的错误信息的分类法，因此我们能够证明哪种错误信息被主要采用，并且主要拒绝。
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.
