We present a novel collection of news articles originating from fake and real news media sources for the analysis and prediction of news virality. Unlike existing fake news datasets which either contain claims or news article headline and body, in this collection each article is supported with a Facebook engagement count which we consider as an indicator of the article virality. In addition we also provide the article description and thumbnail image with which the article was shared on Facebook. These images were automatically annotated with object tags and color attributes. Using cloud based vision analysis tools, thumbnail images were also analyzed for faces and detected faces were annotated with facial attributes. We empirically investigate the use of this collection on an example task of article virality prediction.

本文介绍了一种新颖的新闻文章网站数据集，该数据集来自于假新闻和真实新闻媒体，用于分析和预测新闻的病毒性。该数据集不同于现有的假新闻数据集，每篇文章的Facebook参与计数作为文章病毒性的指标，还提供了文章描述和缩略图图像。使用基于云的视觉分析工具，缩略图图像还进行了面部识别和颜色属性标注。文章还探讨了在文章病毒性预测中使用该数据集的实证研究。

Evons: 用于虚假和真实新闻传播效应分析和预测的数据集