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

Bi-Directional Recurrent Neural Ordinary Differential Equations for Social Media Text Classification