Deepfake on Social Media: Harnessing Deep Learning for Identifying Falsified Tweet

Recent advancements in natural language production have enabled the creation of sophisticated deepfake social media messages, posing a significant threat to public discourse. In response, this study focuses on the development of reliable detection methods for identifying automated text on websites such as Twitter. Leveraging Tweepfake, a publicly accessible dataset, a simple deep learning model employing tf-tf-idf, word2vec and tokenizer from keras library word embeddings and a typical architecture for a convolutional neural network (CNN) is proposed. Comparative analysis against baseline methods, including various feature-based approaches and various forms of deep learning, such Long Short-Term Memory (LSTM), demonstrates the suggested method's greater performance. Experimental results showcase an impressive accuracy of 91% in accurately classifying tweet data either bot-generated or human-generated. This research contributes to the ongoing efforts to combat the proliferation of deepfake content on social media platforms.