Classifying Human-Generated and AI-Generated Election Claims in Social Media

Alphaeus Dmonte, Marcos Zampieri, Kevin Lybarger, Massimiliano Albanese, Genya Coulter

2024

Abstract

Politics is one of the most prevalent topics discussed on social media platforms, particularly during major election cycles, where users engage in conversations about candidates and electoral processes. Malicious actors may use this opportunity to disseminate misinformation to undermine trust in the electoral process. The emergence of Large Language Models (LLMs) exacerbates this issue by enabling malicious actors to generate misinformation at an unprecedented scale. Artificial intelligence (AI)-generated content is often indistinguishable from authentic user content, raising concerns about the integrity of information on social networks. In this paper, we present a novel taxonomy for characterizing election-related claims. This taxonomy provides an instrument for analyzing election-related claims, with granular categories related to jurisdiction, equipment, processes, and the nature of claims. We introduce ElectAI, a novel benchmark dataset comprising 9,900 tweets, each labeled as human- or AI-generated. We annotated a subset of 1,550 tweets using the proposed taxonomy to capture the characteristics of election-related claims. We explored the capabilities of LLMs in extracting the taxonomy attributes and trained various machine learning models using ElectAI to distinguish between human-and AI-generated posts and identify the specific LLM variant.

Paper Citation

in Harvard Style

Dmonte A., Zampieri M., Lybarger K., Albanese M. and Coulter G. (2024). Classifying Human-Generated and AI-Generated Election Claims in Social Media. In Proceedings of the 21st International Conference on Security and Cryptography - Volume 1: SECRYPT; ISBN 978-989-758-709-2, SciTePress, pages 237-248. DOI: 10.5220/0012797900003767

in Bibtex Style

@conference{secrypt24,
author={Alphaeus Dmonte and Marcos Zampieri and Kevin Lybarger and Massimiliano Albanese and Genya Coulter},
title={Classifying Human-Generated and AI-Generated Election Claims in Social Media},
booktitle={Proceedings of the 21st International Conference on Security and Cryptography - Volume 1: SECRYPT},
year={2024},
pages={237-248},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012797900003767},
isbn={978-989-758-709-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 21st International Conference on Security and Cryptography - Volume 1: SECRYPT
TI - Classifying Human-Generated and AI-Generated Election Claims in Social Media
SN - 978-989-758-709-2
AU - Dmonte A.
AU - Zampieri M.
AU - Lybarger K.
AU - Albanese M.
AU - Coulter G.
PY - 2024
SP - 237
EP - 248
DO - 10.5220/0012797900003767
PB - SciTePress