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Navigating the Kaleidoscope of COVID-19 Misinformation Using Deep Learning ...
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Abstract:
Anthology paper link: https://aclanthology.org/2021.emnlp-main.485/ 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 ...
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Keyword:
Computational Linguistics; Covid-19; Deep Learning; Language Models; Machine Learning; Machine Learning and Data Mining; Natural Language Processing
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URL: https://dx.doi.org/10.48448/yyza-sr36 https://underline.io/lecture/37959-navigating-the-kaleidoscope-of-covid-19-misinformation-using-deep-learning
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