  Breaking news leads to situations of fast-paced reporting in social media,
producing all kinds of updates related to news stories, albeit with the caveat
that some of those early updates tend to be rumours, i.e., information with an
unverified status at the time of posting. Flagging information that is
unverified can be helpful to avoid the spread of information that may turn out
to be false. Detection of rumours can also feed a rumour tracking system that
ultimately determines their veracity. In this paper we introduce a novel
approach to rumour detection that learns from the sequential dynamics of
reporting during breaking news in social media to detect rumours in new
stories. Using Twitter datasets collected during five breaking news stories, we
experiment with Conditional Random Fields as a sequential classifier that
leverages context learnt during an event for rumour detection, which we compare
with the state-of-the-art rumour detection system as well as other baselines.
In contrast to existing work, our classifier does not need to observe tweets
querying a piece of information to deem it a rumour, but instead we detect
rumours from the tweet alone by exploiting context learnt during the event. Our
classifier achieves competitive performance, beating the state-of-the-art
classifier that relies on querying tweets with improved precision and recall,
as well as outperforming our best baseline with nearly 40% improvement in terms
of F1 score. The scale and diversity of our experiments reinforces the
generalisability of our classifier.
