  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 that consists of 9,900 tweets,
each labeled as human- or AI-generated. For AI-generated tweets, the specific
LLM variant that produced them is specified. 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.
