发表机构
L3Cube-Labs（L3Cube实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对印度公共事件虚假信息检测缺乏文化感知基准的问题，构建了含14646条记录的BharatGather数据集，为开发相关检测系统及评估其性能提供了基准。
AI 中文摘要
大型公共事件，如宗教节日、政治集会和文化集会，正越来越容易受到虚假信息快速传播的影响，对公共安全和社会凝聚力构成重大风险。虽然自动假新闻检测在方法上取得了显著进展，但现有基准往往无法捕捉印度背景下特有的社会文化细微差别和特定事件动态。本文介绍BharatGather，这是一个精心整理的多源数据集，专门为印度大型集会生态系统内的二元虚假信息分类而设计。该语料库包含14646条记录，通过混合流程构建，涉及对知名事实核查平台的系统性网页抓取、多媒体文本提取以及大语言模型（LLM）介导的合成扩充，以确保叙事多样性。通过提供针对印度事件感知虚假信息独特复杂性量身定制的资源，这项工作促进了文化感知检测系统的开发，并为在高风险公共环境中评估其性能建立了严格的基准。
英文摘要
Large-scale public events, such as religious festivals, political rallies, and cultural gatherings, are increasingly vulnerable to the rapid dissemination of misinformation, posing substantial risks to public safety and social cohesion. While automated fake news detection has seen significant methodological progress, existing benchmarks frequently fail to capture the socio-cultural nuances and event-specific dynamics characteristic of the Indian context. This paper introduces BharatGather, a curated, multi-source dataset specifically engineered for binary misinformation classification within the ecosystem of Indian mass gatherings. The corpus comprises 14,646 records constructed through a hybrid pipeline involving systematic web scraping of prominent fact-checking platforms, multimedia transcript extraction, and Large Language Model (LLM)-mediated synthetic augmentation to ensure narrative diversity. By providing a resource tailored to the unique complexities of event-aware misinformation in India, this work facilitates the development of culturally informed detection systems and establishes a rigorous benchmark for evaluating their performance in high-stakes public environments.
