• Y.Swathi
• Shaik Karishma
• Shaik Tabassum
• Mohammed Mubashera
• M Pravallika

The phenomenon of Fake news is experiencing rapid and growing progress with the evolution of the means of communication and Socialmedia. It is believed that mainstream media platforms are publishing fake news to grasp the attention of readers; most likely, it is done to increase the number of visitors on that particular page so that the page could claim more advertisements with an increasing number of visitors. There is much scope to check the reality of the news received from various sources like websites, blogs, and e-content. To identify fake news, some application in real-time is needed. This paper proposes an efficient method to detect fake news with better accuracy by using the available data set to detect whether the news is FAKE or REAL. We propose an attention-based convolutional bidirectional long short-term memory (AC-BiLSTM) approach

Fake news detection, Deeplearning, Bilstm (Bidirectional Long Short-Term Memory), Training data, disinformation, misinformation, Lstm, Multi class classification, fake news.

"FAKE NEWS DETECTION USING DEEP LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 10, page no.172-179, October-2024, Available :http://www.jetir.org/papers/JETIRGN06020.pdf

"FAKE NEWS DETECTION USING DEEP LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 10, page no. pp172-179, October-2024, Available at : http://www.jetir.org/papers/JETIRGN06020.pdf