  Recent work have done a good job in modeling rumors and detecting them over
microblog streams. However, the performance of their automatic approaches are
not relatively high when looking early in the diffusion. A first intuition is
that, at early stage, most of the aggregated rumor features (e.g., propagation
features) are not mature and distinctive enough. The objective of rumor
debunking in microblogs, however, are to detect these misinformation as early
as possible. In this work, we leverage neural models in learning the hidden
representations of individual rumor-related tweets at the very beginning of a
rumor. Our extensive experiments show that the resulting signal improves our
classification performance over time, significantly within the first 10 hours.
To deepen the understanding of these low and high-level features in
contributing to the model performance over time, we conduct an extensive study
on a wide range of high impact rumor features for the 48 hours range. The end
model that engages these features are shown to be competitive, reaches over 90%
accuracy and out-performs strong baselines in our carefully cured dataset.
