COVID-19 fake news detection based on long short-term memory model

Yuxuan Huang

doi:10.1117/12.2628171

22 April 2022 COVID-19 fake news detection based on long short-term memory model

Yuxuan Huang

Proceedings Volume 12163, International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2021); 121631O (2022) https://doi.org/10.1117/12.2628171
Event: International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2021), 2021, Nanjing, China

Abstract

Since a wide-range of fake news concerning the COVID-19 virus spreads fast and without restraint, many pessimistic effects come along with it, disturbing people's daily life and interfering with the real news' distribution. To improve the situation nowadays, our study tries to come up with an idea of limiting the spread of fake news through detecting, identifying and classifying it. Such an objective is realized using a dataset named COVID-19 Fake News Dataset from the website of Mendeley Data which was delivered in early 2021. LSTM is also applied to build a related model to do fake news detection. As to the study result, our performance parameters include the value of accuracy, precision, etc. Additionally, we use the loss curve and confusion matrix to analyze the results and discuss accordingly. In conclusion, our research provides strategic references based on the LSTM model to solve problems connected with fake news detection on the COVID-19 virus.

Citation Download Citation

Yuxuan Huang "COVID-19 fake news detection based on long short-term memory model", Proc. SPIE 12163, International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2021), 121631O (22 April 2022); https://doi.org/10.1117/12.2628171

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