Along with COVID-19 pandemic we are also fighting an `infodemic'. Fake news and rumors are rampant on social media. Believing in rumors can cause significant harm. This is further exacerbated at the time of a pandemic. To tackle this, we curate and release a manually annotated dataset of 10,700 social media posts and articles of real and fake news on COVID-19. We benchmark the annotated dataset with four machine learning baselines - Decision Tree, Logistic Regression , Gradient Boost , and Support Vector Machine (SVM). We obtain the best performance of 93.46\% F1-score with SVM. The data and code is available at: https://github.com/parthpatwa/covid19-fake-news-dectection

翻译：在COVID-19大流行的同时,我们还在与“信息19大流行”作斗争。在社交媒体上,假消息和谣言十分猖獗。相信谣言会造成重大伤害。在大流行病发生时,情况会进一步恶化。为了解决这个问题,我们编辑和发行一个人工附加说明的数据集,其中包括10 700个社交媒体文章和COVID-19上真实和假新闻的文章。我们用四个机器学习基线来衡量附加说明的数据集——决策树、后勤倒退、渐进运动和辅助病媒机器(SVM)。我们获得了93.46 ⁇ F1核心与SVM的最佳性能。数据和代码见:https://github.com/parthpatwa/covid19-fake-news-dections-dection。