Analyzing Sustainability Messaging in Large-Scale Corporate Social Media
This work addresses the challenge of scalable analysis of ambiguous corporate sustainability communication for researchers and practitioners, though it is incremental as it applies existing models to a new domain.
The paper tackled the problem of analyzing corporate sustainability messaging on social media by introducing a multimodal pipeline using large foundation models to annotate tweets for alignment with Sustainable Development Goals and uncover visual patterns, revealing sectoral differences, temporal trends, and associations with ESG risks and consumer engagement.
In this work, we introduce a multimodal analysis pipeline that leverages large foundation models in vision and language to analyze corporate social media content, with a focus on sustainability-related communication. Addressing the challenges of evolving, multimodal, and often ambiguous corporate messaging on platforms such as X (formerly Twitter), we employ an ensemble of large language models (LLMs) to annotate a large corpus of corporate tweets on their topical alignment with the 17 Sustainable Development Goals (SDGs). This approach avoids the need for costly, task-specific annotations and explores the potential of such models as ad-hoc annotators for social media data that can efficiently capture both explicit and implicit references to sustainability themes in a scalable manner. Complementing this textual analysis, we utilize vision-language models (VLMs), within a visual understanding framework that uses semantic clusters to uncover patterns in visual sustainability communication. This integrated approach reveals sectoral differences in SDG engagement, temporal trends, and associations between corporate messaging, environmental, social, governance (ESG) risks, and consumer engagement. Our methods-automatic label generation and semantic visual clustering-are broadly applicable to other domains and offer a flexible framework for large-scale social media analysis.