  Little attention has been paid on \underline{EA}rly \underline{R}umor
\underline{D}etection (EARD), and EARD performance was evaluated
inappropriately on a few datasets where the actual early-stage information is
largely missing. To reverse such situation, we construct BEARD, a new
\underline{B}enchmark dataset for \underline{EARD}, based on claims from
fact-checking websites by trying to gather as many early relevant posts as
possible. We also propose HEARD, a novel model based on neural
\underline{H}awkes process for \underline{EARD}, which can guide a generic
rumor detection model to make timely, accurate and stable predictions.
Experiments show that HEARD achieves effective EARD performance on two commonly
used general rumor detection datasets and our BEARD dataset.
