Automated false claims detection using deep neural networks

Abstract

Recently false claims and misinformation have become rampant in the web, affecting election outcomes, stock markets, and various other societal issues. Consequently, fact-checking and claim verification websites such as snopes.com are becoming increasingly popular and are also being integrated into news search engines such as Google news. However, these websites require expert analysis which is slow and not scalable. Many recent papers have proposed machine learning methods using handpicked linguistic and source-based cues to automate the claim verification process. In this thesis, we propose deep neural models which avoid tedious feature engineering and strong assumptions and yet detect false claims with high accuracy. To achieve this, we propose a hybrid model which combines textual content of the news articles as well as the reactions they receive in social media forums such as Reddit. Using large-scale manually curated data from fact-checking websites such as snopes.com, politifact.com and emergent.info we perform extensive experiments to show that our models outperform the state-of-the-art CRF-based models.