Navigating the Kaleidoscope of COVID-19 Misinformation Using Deep Learning

• URL: http://arxiv.org/abs/2110.15703v1
• Date: Sun, 19 Sep 2021 15:49:25 GMT
• Title: Navigating the Kaleidoscope of COVID-19 Misinformation Using Deep Learning
• Authors: Yuanzhi Chen and Mohammad Rashedul Hasan
• Abstract summary: We propose an effective model to capture both the local and global context of the target domain. 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. 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.
• Score: 0.76146285961466
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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