The spread of election misinformation and harmful political content conveys misleading narratives and poses a serious threat to democratic integrity. Detecting harmful content at early stages is essential for understanding and potentially mitigating its downstream spread. In this study, we introduce USE24-XD, a large-scale dataset of nearly 100k posts collected from X (formerly Twitter) during the 2024 U.S. presidential election cycle, enriched with spatio-temporal metadata. To substantially reduce the cost of manual annotation while enabling scalable categorization, we employ six large language models (LLMs) to systematically annotate posts across five nuanced categories: Conspiracy, Sensationalism, Hate Speech, Speculation, and Satire. We validate LLM annotations with crowdsourcing (n = 34) and benchmark them against human annotators. Inter-rater reliability analyses show comparable agreement patterns between LLMs and humans, with LLMs exhibiting higher internal consistency and achieving up to 0.90 recall on Speculation. We apply a wisdom-of-the-crowd approach across LLMs to aggregate annotations and curate a robust multi-label dataset. 60% of posts receive at least one label. We further analyze how human annotator demographics, including political ideology and affiliation, shape labeling behavior, highlighting systematic sources of subjectivity in judgments of harmful content. The USE24-XD dataset is publicly released to support future research.

翻译：选举错误信息和有害政治内容的传播传递误导性叙事，对民主完整性构成严重威胁。在早期阶段检测有害内容对于理解并可能减轻其下游传播至关重要。本研究介绍了USE24-XD数据集，该数据集包含从X平台（原Twitter）在2024年美国总统选举周期内收集的近10万条推文，并附有时空元数据。为大幅降低人工标注成本同时实现可扩展分类，我们采用六个大型语言模型（LLM）系统性地对推文进行五个精细类别的标注：阴谋论、煽情主义、仇恨言论、推测性内容和讽刺作品。我们通过众包（n=34）验证LLM标注结果，并以人工标注为基准进行比较。评分者间信度分析显示LLM与人类标注者具有可比的一致性模式，其中LLM展现出更高的内部一致性，在推测性内容类别上召回率最高达0.90。我们采用群体智慧方法聚合多个LLM的标注结果，构建了稳健的多标签数据集。60%的推文获得至少一个标签。我们进一步分析了人类标注者的人口统计学特征（包括政治意识形态和党派归属）如何影响标注行为，揭示了有害内容判断中系统性的主观性来源。USE24-XD数据集已公开发布以支持未来研究。