Deep Learning and Quick Text Embedding’s for Deep Fake Detection

Abstract

The legitimacy of social media content, especially textual content, is facing significant challenges because to the spread of deep fake technologies.  This study shows how to recognise machine-generated tweets using deep learning and fast text embeddings.  We create a dataset that displays different language styles and contains both authentic and fraudulent tweets.  Rapid text embeddings facilitate effective feature extraction by enabling a deep learning network to understand semantic nuances.  The machine can consistently and precisely distinguish between authentic and fraudulent content by using this dataset for training and validation.  The findings demonstrate that by making deep fake text easier to identify, this technique aids in the battle against social media misinformation. According to this research, automated technologies are essential for safeguarding online discourse against deep fake threats.