  Recently a lot of progress has been made in rumor modeling and rumor
detection for micro-blogging streams. However, existing automated methods do
not perform very well for early rumor detection, which is crucial in many
settings, e.g., in crisis situations. One reason for this is that aggregated
rumor features such as propagation features, which work well on the long run,
are - due to their accumulating characteristic - not very helpful in the early
phase of a rumor. In this work, we present an approach for early rumor
detection, which leverages Convolutional Neural Networks for learning the
hidden representations of individual rumor-related tweets to gain insights on
the credibility of each tweets. We then aggregate the predictions from the very
beginning of a rumor to obtain the overall event credits (so-called wisdom),
and finally combine it with a time series based rumor classification model. Our
extensive experiments show a clearly improved classification performance within
the critical very first hours of a rumor. For a better understanding, we also
conduct an extensive feature evaluation that emphasized on the early stage and
shows that the low-level credibility has best predictability at all phases of
the rumor lifetime.
