Companies spend large amounts of money on public relations campaigns to project a positive brand image. However, sometimes there is a mismatch between what they say and what they do. Oil & gas companies, for example, are accused of "greenwashing" with imagery of climate-friendly initiatives. Understanding the framing, and changes in framing, at scale can help better understand the goals and nature of public relations campaigns. To address this, we introduce a benchmark dataset of expert-annotated video ads obtained from Facebook and YouTube. The dataset provides annotations for 13 framing types for more than 50 companies or advocacy groups across 20 countries. Our dataset is especially designed for the evaluation of vision-language models (VLMs), distinguishing it from past text-only framing datasets. Baseline experiments show some promising results, while leaving room for improvement for future work: GPT-4.1 can detect environmental messages with 79% F1 score, while our best model only achieves 46% F1 score on identifying framing around green innovation. We also identify challenges that VLMs must address, such as implicit framing, handling videos of various lengths, or implicit cultural backgrounds. Our dataset contributes to research in multimodal analysis of strategic communication in the energy sector.

翻译：企业在公共关系活动中投入大量资金以塑造积极的品牌形象。然而，其宣传内容与实际行为之间常存在脱节。以石油天然气企业为例，其常因使用气候友好倡议的视觉形象而被指控进行"绿色漂洗"。大规模解析框架构建策略及其演变规律，有助于深入理解公共关系活动的目标与本质。为此，我们构建了一个专家标注的基准数据集，包含从Facebook和YouTube平台采集的视频广告。该数据集涵盖20个国家50余家企业及倡议组织的13种框架类型标注。本数据集专为视觉-语言模型评估而设计，区别于以往纯文本框架数据集。基线实验显示：GPT-4.1检测环境信息的F1分数达79%，而我们最佳模型在识别绿色创新框架任务中仅获得46%的F1分数，表明未来研究仍有提升空间。研究同时揭示了视觉-语言模型需应对的挑战，包括隐含框架解析、多时长视频处理及隐性文化背景理解。本数据集为能源领域战略传播的多模态分析研究提供了重要资源。