A Multimodal Benchmark for Framing of Oil & Gas Advertising and Potential Greenwashing Detection
This addresses the issue of analyzing strategic communication for researchers and practitioners in energy and AI, though it is incremental as it extends existing text-only framing datasets to multimodal evaluation.
The paper tackles the problem of detecting greenwashing in oil and gas advertising by introducing a multimodal benchmark dataset of expert-annotated video ads, with baseline experiments showing GPT-4.1 achieving 79% F1 score for environmental messages but the best model only 46% F1 for green innovation framing.
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.