  Social media communications are becoming increasingly prevalent; some useful,
some false, whether unwittingly or maliciously. An increasing number of rumours
daily flood the social networks. Determining their veracity in an autonomous
way is a very active and challenging field of research, with a variety of
methods proposed. However, most of the models rely on determining the
constituent messages' stance towards the rumour, a feature known as the "wisdom
of the crowd". Although several supervised machine-learning approaches have
been proposed to tackle the message stance classification problem, these have
numerous shortcomings. In this paper we argue that semi-supervised learning is
more effective than supervised models and use two graph-based methods to
demonstrate it. This is not only in terms of classification accuracy, but
equally important, in terms of speed and scalability. We use the Label
Propagation and Label Spreading algorithms and run experiments on a dataset of
72 rumours and hundreds of thousands messages collected from Twitter. We
compare our results on two available datasets to the state-of-the-art to
demonstrate our algorithms' performance regarding accuracy, speed and
scalability for real-time applications.
