Deepfake tweets automatic detection

TL;DR

This paper develops and evaluates machine learning methods using NLP techniques to detect AI-generated DeepFake tweets, aiming to combat misinformation and improve online information trustworthiness.

Contribution

It introduces a novel approach leveraging NLP and machine learning models trained on the TweepFake dataset for DeepFake tweet detection.

Findings

Effective models identified for DeepFake detection

Improved accuracy over baseline methods

Enhanced understanding of linguistic features in DeepFake texts

Abstract

This study addresses the critical challenge of detecting DeepFake tweets by leveraging advanced natural language processing (NLP) techniques to distinguish between genuine and AI-generated texts. Given the increasing prevalence of misinformation, our research utilizes the TweepFake dataset to train and evaluate various machine learning models. The objective is to identify effective strategies for recognizing DeepFake content, thereby enhancing the integrity of digital communications. By developing reliable methods for detecting AI-generated misinformation, this work contributes to a more trustworthy online information environment.