Navigating the Kaleidoscope of COVID-19 Misinformation Using Deep Learning

TL;DR

This paper investigates the limitations of deep Transformer models in detecting COVID-19 misinformation on social media and proposes a hierarchical approach combining shallow models and CNNs for better generalization.

Contribution

It introduces a novel hierarchical model that integrates shallow domain-specific models with CNNs to improve misinformation detection across diverse COVID-19 social media data.

Findings

Transformer models mainly capture local context and lack generalization.

Combining shallow models with CNNs enhances global and local context extraction.

Proposed approach outperforms pure deep learning models in generalization.

Abstract

Irrespective of the success of the deep learning-based mixed-domain transfer learning approach for solving various Natural Language Processing tasks, it does not lend a generalizable solution for detecting misinformation from COVID-19 social media data. Due to the inherent complexity of this type of data, caused by its dynamic (context evolves rapidly), nuanced (misinformation types are often ambiguous), and diverse (skewed, fine-grained, and overlapping categories) nature, it is imperative for an effective model to capture both the local and global context of the target domain. By conducting a systematic investigation, we show that: (i) the deep Transformer-based pre-trained models, utilized via the mixed-domain transfer learning, are only good at capturing the local context, thus exhibits poor generalization, and (ii) a combination of shallow network-based domain-specific models and convolutional neural networks can efficiently extract local as well as global context directly from the target data in a hierarchical fashion, enabling it to offer a more generalizable solution.