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

Tamire, M. and Anumasa, S. and Srijith, P K (2022) Bi-Directional Recurrent Neural Ordinary Differential Equations for Social Media Text Classification. In: 2nd WIT-Workshop On Deriving Insights From User-Generated Text, WIT 2022, 27 May 2022, Dublin.

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Abstract

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. Â© 2022 Association for Computational Linguistics.

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IITH Creators:

IITH Creators	ORCiD
Srijith, P K	https://orcid.org/0000-0002-2820-0835

Item Type:

Conference or Workshop Item (Paper)

Uncontrolled Keywords:

Bi-directional; Classification models; Forward-and-backward; Information flows; Model-based OPC; Post classification; Posting time; Sequence classification; Social media; Text classification

Subjects:

Computer science

Divisions:

Department of Computer Science & Engineering

Depositing User:

. LibTrainee 2021

Date Deposited:

16 Sep 2022 11:59

Last Modified:

16 Sep 2022 11:59

URI:

http://raiithold.iith.ac.in/id/eprint/10600