论文标题
战斗不良：COVID-19假新闻数据集
Fighting an Infodemic: COVID-19 Fake News Dataset
论文作者
论文摘要
除199年大流行时，我们还在与“流行病”作斗争。假新闻和谣言在社交媒体上猖ramp。相信谣言会造成重大伤害。在大流行时，这进一步加剧了。为了解决这个问题，我们策划并发布了一个手动注释的数据集，其中包含10,700个社交媒体帖子以及Covid-19的真实和虚假新闻的文章。我们使用四个机器学习基线的注释数据集（决策树，逻辑回归，梯度提升和支持向量机（SVM））对该数据集进行基准测试。我们通过SVM获得93.46％F1得分的最佳性能。数据和代码可在以下网址获得：https：//github.com/parthpatwa/covid19-fake-news-dectection
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
