CED: Credible Early Detection of Social Media Rumors

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 percent, with better accuracy performance than all state-of-the-art baselines.

Automated summary

Rumors spread quickly on online social media, and there is a need for efficient methods to automatically detect rumors. This paper proposes a novel early rumor detection model, Credible Early Detection (CED), which significantly reduces the time span for prediction with better accuracy performance than existing methods.