Deepfake Detection on Social Media: Leveraging Deep Learning and Fast Text Embeddings for Identifying Machine-Generated Tweets

Abstract

Recent improvements in natural language generation offer a new means to influence public opinion on social media. Moreover, developments in language modelling have considerably augmented the generative capacities of deep neural models, equipping them with improved competencies for content creation. As a result, text-generative models have gained significant potency, enabling adversaries to leverage these capabilities to enhance social bots, facilitating the creation of authentic deepfake postings and swaying public conversation. In order to address this issue, trustworthy and efficient techniques for detecting social media deepfakes are essential. Automated text on Twitter has been identified in recent studies. To determine if tweets are generated by humans or bots, this study employs the publicly available Tweepfake dataset and a basic deep learning model that makes use of word embeddings. Using a CNN architecture, FastText word embeddings can detect deepfake tweets. This study utilised many machine learning models as baseline approaches to demonstrate the higher performance of the proposed approach. Some of the fundamental techniques used here were Term Frequency, Fast Text subword embeddings, FastText, and Term Frequency-Inverse Document Frequency. Additional comparisons with other DL models, such as CNN-LSTM and LSTM, demonstrate the effectiveness and benefits of the suggested approach in precisely resolving the issue. The experimental results demonstrate that Twitter data may be efficiently and accurately classified with a 93% accuracy rate using CNN architecture and FastText embeddings.