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

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

Jatinder Kaur, Palakurthy Sai Vinay, Gandlapati Sunil Kumar Reddy, Paripelli Venkata Shiva Sai, Vasipalli Madhan Mohan Reddy

School of Computer Science and Engineering
Lovely Professional University
Punjab, India

Abstract: 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.

Keywords: Deep Learning, Machine Learning, Deepfake Detection, Text Classification