A Multimodal Framework for Analyzing Engagement With Green Social Media Influencers

ABSTRACT

In light of growing concerns about environmental degradation and unsustainable consumption patterns, effective communication about sustainability has become crucial in encouraging more environmentally conscious behavior. With their ability to connect with large audiences, social media influencers hold considerable potential to foster environmental awareness and promote sustainable lifestyles. This study explores how influencers communicate sustainability and how they can produce content that maximizes audience engagement. First, a systematic literature review identifies key research gaps and synthesizes relevant insights to inform the analysis of influencer‐driven sustainability discourse. Building on this foundation, the study applies a two‐phase, data‐driven approach. Using a dataset of 2858 Instagram posts from 25 green influencers, we apply Latent Dirichlet Allocation (LDA) to identify prevalent themes published by green influencers and to track their evolution over time. The second phase involves developing a multimodal predictive model that estimates engagement levels by integrating textual and visual embeddings with structured posting metadata. The findings have direct implications for content strategy: lifestyle and fashion‐oriented sustainability themes consistently attracted higher engagement, while technical subjects such as gardening and nutrition received less interaction. Over time, influencers' content exhibited clearer topic delineation, indicating a maturation of sustainability narratives. Furthermore, caption content was found to be the strongest predictor of engagement, though visual and contextual features also added value. This study offers a practical framework for enhancing engagement through targeted, data‐informed content strategies. These insights can inform the work of influencers, brands and campaign designers looking to maximize the impact of environmentally focused messaging.