DEEP FAKE ON SOCIAL MEDIA: FOR IDENTIFYING MACHINE GENERATED TWEETS

Recent advancements in natural language production provide an additional tool to manipulate public opinion on social media. Furthermore, advancements in language modelling have significantly strengthened the generative capabilities of deep neural models, empowering them with enhanced skills for content generation. Consequently, text-generative models have become increasingly powerful allowing adversaries to use these remarkable abilities to boost social bots, allowing them to generate realistic deepfake posts and influence the discourse among the public. To address this problem, the development of reliable and accurate deepfake social media message detecting methods is important. Under this consideration, current research addresses the identification of machine-generated text on social networks like Twitter. In this study, a straightforward deep learning model in combination with word embeddings was employed for the classification of tweets as human-generated or bot-generated using a publicly available Tweep fake dataset. A conventional Convolutional Neural Network (CNN) architecture is devised, leveraging Fast text word embeddings, to undertake the task of identifying deepfake tweets. To showcase the superior performance of the proposed method, we employed several machine learning models as baseline methods for comparison. These baseline methods utilized various features, including term frequency, term frequency-inverse document frequency, Fast Text, and Fast Text sub word embeddings.