Theoretical and Natural Science
- The Open Access Proceedings Series for Conferences
Theoretical and Natural Science
Vol. 2, 20 February 2023
Open
Access | Article
The Covid-19 Disinformation Detection on Social Media Using the NLP Approaches
* Author to whom correspondence should be addressed.
Abstract
Artificial intelligence has emerged with big data technologies in natural language processing and been applied to creative solutions for overload information especially around the time of the COVID-19 epidemic. This paper provides a comprehensive review of research dedicated to applications of artificial intelligence in misinformation detection. This work organizes the necessary background material for COVID-19-related misinformation detection in NLP, concentrating on the transfer learning technique. Database, data preparation, and modeling make up the major body of information. In the part of modeling, it will merge the attributes of the pre-trained model with the specifical task scenario to explain and present pertinent comments on the future model's improvement under the task scenario. This research will benefit the decision-making and information screen for people's inability to distinguish truth from fiction during the COVID-19 pandemic.
Keywords
Language Model, BERT, Natural Language Processing, COVID-19, XLNet
References
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Data Availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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- Volume Title
- Proceedings of the International Conference on Computing Innovation and Applied Physics (CONF-CIAP 2022)
- ISBN (Print)
- 978-1-915371-13-3
- ISBN (Online)
- 978-1-915371-14-0
- Published Date
- 20 February 2023
- Series
- Theoretical and Natural Science
- ISSN (Print)
- 2753-8818
- ISSN (Online)
- 2753-8826
- DOI
- 10.54254/2753-8818/2/20220188
- Copyright
- © 2023 The Author(s)
- Open Access
- This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited