  Rumors spread dramatically fast through online social media services, and
people are exploring methods to detect rumors automatically. Existing methods
typically learn semantic representations of all reposts to a rumor candidate
for prediction. However, it is crucial to efficiently detect rumors as early as
possible before they cause severe social disruption, which has not been well
addressed by previous works. In this paper, we present a novel early rumor
detection model, Credible Early Detection (CED). By regarding all reposts to a
rumor candidate as a sequence, the proposed model will seek an early
point-in-time for making a credible prediction. We conduct experiments on three
real-world datasets, and the results demonstrate that our proposed model can
remarkably reduce the time span for prediction by more than 85%, with better
accuracy performance than all state-of-the-art baselines.
