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.- Anthology ID:
- 2021.emnlp-main.485
- Volume:
- Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
- Month:
- November
- Year:
- 2021
- Address:
- Online and Punta Cana, Dominican Republic
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 6000–6017
- Language:
- URL:
- https://aclanthology.org/2021.emnlp-main.485
- DOI:
- 10.18653/v1/2021.emnlp-main.485
- Cite (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.
- Cite (Informal):
- Navigating the Kaleidoscope of COVID-19 Misinformation Using Deep Learning (Chen & Hasan, EMNLP 2021)
- PDF:
- https://preview.aclanthology.org/paclic-22-ingestion/2021.emnlp-main.485.pdf