  The proliferation of social media in communication and information
dissemination has made it an ideal platform for spreading rumors. Automatically
debunking rumors at their stage of diffusion is known as \textit{early rumor
detection}, which refers to dealing with sequential posts regarding disputed
factual claims with certain variations and highly textual duplication over
time. Thus, identifying trending rumors demands an efficient yet flexible model
that is able to capture long-range dependencies among postings and produce
distinct representations for the accurate early detection. However, it is a
challenging task to apply conventional classification algorithms to rumor
detection in earliness since they rely on hand-crafted features which require
intensive manual efforts in the case of large amount of posts. This paper
presents a deep attention model on the basis of recurrent neural networks (RNN)
to learn \textit{selectively} temporal hidden representations of sequential
posts for identifying rumors. The proposed model delves soft-attention into the
recurrence to simultaneously pool out distinct features with particular focus
and produce hidden representations that capture contextual variations of
relevant posts over time. Extensive experiments on real datasets collected from
social media websites demonstrate that (1) the deep attention based RNN model
outperforms state-of-the-arts that rely on hand-crafted features; (2) the
introduction of soft attention mechanism can effectively distill relevant parts
to rumors from original posts in advance; (3) the proposed method detects
rumors more quickly and accurately than competitors.
