Convergence or divergence? A cross-platform analysis of climate change visual content categories, features, and social media engagement on Twitter and Instagram

Abstract

Advocacy organizations increasingly leverage social media and visuals to communicate complex climate issues. By examining an extensive dataset of visual posts collected from five organization accounts on two multimodal social media platforms, Twitter and Instagram, we conducted a cross-platform comparison of visual content categories and visual features related to climate change. Through deep-learning-based unsupervised image clustering, we discovered that visual content on both platforms could be broadly classified into five categories: infographics/captioned images, nature landscape/wildlife, climate activism, technology, and data visualization. However, these categories were not equally represented on each platform. Instagram featured more nature landscape/wildlife content, while Twitter showed more infographics/captioned images and data visualization. Through computational visual analysis, we found that the two platforms also presented significant differences in overall warm and cool colors, brightness, colorfulness, visual complexity, and presence of faces. Additionally, we identified platform-specific patterns of engagement associated with these categories and features. With the urgency to address climate change, these findings can guide climate advocacy organizations in developing strategies tailored to each platform’s specific characteristics for maximum effectiveness. This study highlights the significance of using computational methods in efficiently uncovering meaningful themes from extensive visual data and quantifying aesthetic features in strategic communication.