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        <title>Navigating the Kaleidoscope of COVID-19 Misinformation Using Deep Learning</title>
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    <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.</abstract>
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    <identifier type="doi">10.18653/v1/2021.emnlp-main.485</identifier>
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%0 Conference Proceedings
%T Navigating the Kaleidoscope of COVID-19 Misinformation Using Deep Learning
%A Chen, Yuanzhi
%A Hasan, Mohammad
%S Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online and Punta Cana, Dominican Republic
%F chen-hasan-2021-navigating
%X 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.
%R 10.18653/v1/2021.emnlp-main.485
%U https://aclanthology.org/2021.emnlp-main.485
%U https://doi.org/10.18653/v1/2021.emnlp-main.485
%P 6000-6017

Markdown (Informal)

[Navigating the Kaleidoscope of COVID-19 Misinformation Using Deep Learning](https://aclanthology.org/2021.emnlp-main.485) (Chen & Hasan, EMNLP 2021)

• Navigating the Kaleidoscope of COVID-19 Misinformation Using Deep Learning (Chen & Hasan, EMNLP 2021)

ACL

• Yuanzhi Chen and Mohammad Hasan. 2021. Navigating the Kaleidoscope of COVID-19 Misinformation Using Deep Learning. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 6000–6017, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.