• Indu Bala
• Dr. Sunita Mahajan
• Dr. Ikvinderpal SIngh

The COVID-19 pandemic has been accompanied by an "infodemic" of fake news. This widespread misinformation poses a serious threat to public health. This paper proposes an optimized deep learning model for identifying COVID-19-related fake news. The model leverages a combination of convolutional neural networks (CNNs) and long short-term memory (LSTM) networks, along with optimization techniques to differentiate between real and fake news. By combining these elements, the proposed model achieves superior performance in detecting COVID-19-related misinformation, paving the way for a more informed and healthier public discourse.

Convolutional Neural Networks (CNNs); Long Short-Term Memory (LSTM) networks; COVID-19; Fake News;

"Detecting COVID-19 Fake News:An Optimized Deep Learning Approach", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 5, page no.l657-l661, May 2025, Available :http://www.jetir.org/papers/JETIR2505C66.pdf

"Detecting COVID-19 Fake News:An Optimized Deep Learning Approach", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 5, page no. ppl657-l661, May 2025, Available at : http://www.jetir.org/papers/JETIR2505C66.pdf