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

TL;DR

This paper introduces a new taxonomy and dataset for classifying election-related social media claims as human- or AI-generated, addressing misinformation challenges amplified by advanced language models.

Contribution

It presents a novel taxonomy for election claims, a large labeled dataset (ElectAI), and models to distinguish between human and AI-generated social media content.

Findings

Successfully created the ElectAI dataset with 9,900 labeled tweets.

Demonstrated machine learning models can effectively differentiate human and AI-generated tweets.

Identified specific LLMs responsible for AI-generated content in social media posts.

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 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.