Automatic Ground Truth Dataset Creation for Fake News Detection in Social Media

Danae Pla Karidi, Harry Nakos, and Yannis Stavrakas
20th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL 2019), Manchester, 14-16 November 2019
Abstract. Fake news has become over the last years one of the most crucial issues for so-cial media platforms, users and news organizations. Therefore, research has fo-cused on developing algorithmic methods to detect misleading content on social media. These approaches are data-driven, meaning that the efficiency of the pro-duced models depends on the quality of the training dataset. Although several ground truth datasets have been created, they suffer from serious limitations and rely heavily on human annotators. In this work, we propose a method for auto-mating as far as possible the process of dataset creation. Such datasets can be subsequently used as training and test data in machine learning classification techniques regarding fake news detection in microblogging platforms, such as Twitter.