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.

本文旨在开发一个自动化、监督的分类器，使用多任务学习来分类疑点话题中每个个体推文中的立场，从而将高争议的流言标记为潜在的虚假信息。我们的分类器基于高斯过程，探究其有效性并对具有不同特征和立场分布变化的两个数据集进行探索性研究。结果表明，我们的方法始终优于其他基线分类器。特别是在估计与给定流言相关的不同类型立场的分布方面，我们的分类器非常有效。这是我们要提出的一种理想特征，用于跟踪流言并向普通 Twitter 用户和专业新闻从业者发出警告。