  Classification of posts in social media such as Twitter is difficult due to
the noisy and short nature of texts. Sequence classification models based on
recurrent neural networks (RNN) are popular for classifying posts that are
sequential in nature. RNNs assume the hidden representation dynamics to evolve
in a discrete manner and do not consider the exact time of the posting. In this
work, we propose to use recurrent neural ordinary differential equations
(RNODE) for social media post classification which consider the time of posting
and allow the computation of hidden representation to evolve in a
time-sensitive continuous manner. In addition, we propose a novel model,
Bi-directional RNODE (Bi-RNODE), which can consider the information flow in
both the forward and backward directions of posting times to predict the post
label. Our experiments demonstrate that RNODE and Bi-RNODE are effective for
the problem of stance classification of rumours in social media.
