Using Gaussian Processes for Rumour Stance Classification in Social Media
This work addresses the challenge of detecting potentially false rumours for Twitter users and news professionals, though it is incremental as it applies an existing method to a specific domain.
The authors tackled the problem of classifying user stances (supporting, denying, questioning) towards rumours in social media tweets using a Gaussian Processes-based multi-task learning classifier, showing it consistently outperforms baselines across two datasets with different characteristics.
Social media tend to be rife with rumours while new reports are released piecemeal during breaking news. Interestingly, one can mine multiple reactions expressed by social media users in those situations, exploring their stance towards rumours, ultimately enabling the flagging of highly disputed rumours as being potentially false. In this work, we set out to develop an automated, supervised classifier that uses multi-task learning to classify the stance expressed in each individual tweet in a rumourous conversation as either supporting, denying or questioning the rumour. Using a classifier based on Gaussian Processes, and exploring its effectiveness on two datasets with very different characteristics and varying distributions of stances, we show that our approach consistently outperforms competitive baseline classifiers. Our classifier is especially effective in estimating the distribution of different types of stance associated with a given rumour, which we set forth as a desired characteristic for a rumour-tracking system that will warn both ordinary users of Twitter and professional news practitioners when a rumour is being rebutted.