DeepTextGuard: Detecting Machine-Written Tweets Using FastText and Deep Learning

Abstract

The proliferation of deepfake technology has raised concerns about the spread of misinformation on social media platforms. In this paper, we propose a deep learning-based approach for detecting deepfake tweets, specifically those generated by machines, to help mitigate the impact of misinformation online. Our approach leverages Fast Text embeddings to represent tweet text and combines them with deep learning models for classification. We first preprocess the tweet text and then use Fast Text embeddings to convert them into dense vector representations. These embeddings capture semantic information about the tweet content, which is crucial for distinguishing between genuine and machine-generated tweets. We then feed these embeddings into a deep learning model, such as a Convolutional Neural Network (CNN) or a Long Short-Term Memory (LSTM) network, to classify the tweets as genuine or machine-generated. The model is trained on a labelled dataset of tweets, where machine-generated tweets are synthesized using state-of-the-art text generation models. Experimental results on a real-world dataset of tweets demonstrate the effectiveness of our approach in detecting machine-generated tweets. Our approach achieves high accuracy and outperforms existing methods for deepfake detection on social media. Overall, our proposed approach provides a promising solution for identifying machine- generated tweets and combating the spread of misinformation on social media platforms. Simple text manipulation techniques can shape false beliefs, and the impact of powerful transformer models needs to be addressed. The dataset contains tweets from human accounts and various bot accounts using techniques such as RNN, LSTM, Markov, and GPT-2. Moreover, the performance of the proposed method is also compared against other deep learning models such as Long short-term memory (LSTM) and CNN- LSTM displaying the effectiveness and highlighting its advantages in accurately addressing the task at hand. Experimental results indicate that the streamlined design of the CNN architecture, coupled with the utilization of FastText embeddings, allowed for efficient and effective classification of the tweet data with a superior 93% accuracy.