Evons: A Dataset for Fake and Real News Virality Analysis and Prediction
This dataset addresses the problem of predicting news virality for researchers and practitioners in misinformation detection, though it is incremental as it builds on existing fake news datasets by adding virality metrics.
The authors introduced Evons, a dataset for analyzing and predicting news virality, which includes fake and real news articles with Facebook engagement counts as virality indicators, along with annotated images and descriptions. They demonstrated its use by empirically testing it on an article virality prediction task.
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.