The rapid rise of large language models has enabled the automated generation of fake news at scale, with content that is increasingly difficult to distinguish from human-written text. In this work, we study AI-generated fake news across multiple domains and develop techniques to detect and characterize such content. We analyze the linguistic and stylistic signatures of machine-generated misinformation, evaluate the performance of detection methods under domain shift, and discuss the implications for combating automated disinformation campaigns.
@misc{nanabala2024unmasking,
title = {Unmasking AI-Generated Fake News Across Multiple Domains},
author = {Chiradeep Nanabala and Chilukuri K. Mohan and Reza Zafarani},
year = {2024},
keywords = {preprint},
howpublished = {Preprints.org},
abstract = {The rapid rise of large language models has enabled the automated generation of fake news at scale, with content that is increasingly difficult to distinguish from human-written text. In this work, we study AI-generated fake news across multiple domains and develop techniques to detect and characterize such content. We analyze the linguistic and stylistic signatures of machine-generated misinformation, evaluate the performance of detection methods under domain shift, and discuss the implications for combating automated disinformation campaigns.},
url = {https://doi.org/10.20944/preprints202405.0686.v1},
note = {May 2024},
}