论文标题
COVMIS-Stance数据集：Twitter上的立场检测COVID-19错误信息
The COVMis-Stance dataset: Stance Detection on Twitter for COVID-19 Misinformation
论文作者
论文摘要
在Covid-19大流行期间，社交媒体上正在传播大量的COVID-19错误信息。我们对Twitter用户对COVID-19错误信息的立场感兴趣。但是，由于大流行的最新性质，只有少数立场检测数据集符合我们的任务。我们已经构建了一个新的立场数据集，该数据集由2631条推文组成，并注释了COVID-19错误信息的立场。在标记数据有限的上下文中，我们通过利用MNLI数据集和两个现有的立场检测数据集（Rumoureval和Covidlies）来微调模型，并评估我们数据集中的模型性能。我们的实验结果表明，当在MNLI数据集上进行微调以及未采样的Rumoureval和Covidlies数据集的组合时，该模型表现最好。我们的代码和数据集可在https://github.com/yanfangh/covid-rumor-stance上公开获取
During the COVID-19 pandemic, large amounts of COVID-19 misinformation are spreading on social media. We are interested in the stance of Twitter users towards COVID-19 misinformation. However, due to the relative recent nature of the pandemic, only a few stance detection datasets fit our task. We have constructed a new stance dataset consisting of 2631 tweets annotated with the stance towards COVID-19 misinformation. In contexts with limited labeled data, we fine-tune our models by leveraging the MNLI dataset and two existing stance detection datasets (RumourEval and COVIDLies), and evaluate the model performance on our dataset. Our experimental results show that the model performs the best when fine-tuned sequentially on the MNLI dataset and the combination of the undersampled RumourEval and COVIDLies datasets. Our code and dataset are publicly available at https://github.com/yanfangh/covid-rumor-stance
