Predicting future rumours

Abstract

Recent uproar of fake news and misinformation on social media platforms has sparked the interest in the scientific community to automatically detect and refute them. The most popular research task to counteract misinformation, Rumour detection, requires repeated signals to reach adequate detection accurate. Consequently, rumour detection recognizes rumours only when they have started spreading and causing harm. We introduce a new task called “rumour prediction” that assesses the possibility of a document arriving from a social media stream becoming a rumour in the future. Note that rumour prediction differentiates itself from rumour detection through instant decision making. This allows refuting misinformation before it spreads and causes harm. Our approach to rumour prediction harnesses content based features in combination with novelty based features and pseudo feedback. Our experiments show that we are able to accurately predict, whether a document will become a rumour in the future. Additionally, we show how rumour prediction can significantly improve the accuracy of state-of-the-art Rumour detection systems.