TATHYA

• DOI: 10.1145/3132847.3133150
• Publication Date: 2017-11-06T13:30:29Z
• Authors: Ayush Patwari; Dan Goldwasser; Saurabh Bagchi
• Keywords: 0202 electrical engineering, electronic engineering, information engineering; 02 engineering and technology; 16. Peace & justice

Fact-checking political discussions has become an essential clog in computational journalism. This task encompasses an important sub-task---identifying the set of statements with 'check-worthy' claims. Previous work has treated this as a simple text classification problem discounting the nuances involved in determining what makes statements check-worthy. We introduce a dataset of political debates from the 2016 US Presidential election campaign annotated using all major fact-checking media outlets and show that there is a need to model conversation context, debate dynamics and implicit world knowledge. We design a multi-classifier system TATHYA, that models latent groupings in data and improves state-of-art systems in detecting check-worthy statements by 19.5% in F1-score on a held-out test set, gaining primarily gaining in Recall.