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This study investigates how social media communications from housing providers influence tenant discourse regarding sustainability, using a novel multidimensional framework. By analyzing 792 posts and 3,197 comments over six years, the authors employed machine learning to classify tenant responses based on communicative intent, sentiment, and semantic relatedness, revealing that tenant comments were more semantically aligned with their providers' posts than with random content. The findings indicate that organizational characteristics significantly shape discourse quality and engagement, with larger housing associations fostering more substantive discussions over time.
Tenant responses to sustainability communications are more aligned with housing providers' posts than random interactions, highlighting the power of organizational messaging in shaping public discourse.
Authorities increasingly rely on social media to advance sustainability transitions, infrastructure investment, and service reform. Yet how citizens respond to these digital communications remains poorly understood. Existing approaches rely on aggregate engagement metrics (e.g., likes), providing limited insight into discourse structure and quality. We developed a data-driven, multidimensional framework to analyse how social media communication shapes the content of discourse, focusing on sustainability-related engagement in Dutch public housing. We analysed 792 posts and 3,197 tenant comments from the Facebook pages of 92 housing providers (2018-2023). A machine-learning pipeline classified comments into recurring discourse configurations across three dimensions - communicative intent, sentiment, and semantic relatedness. Multinomial logistic regression estimated the effects of post-design and organisational characteristics on discourse. Tenant comments were significantly more semantically aligned with their corresponding posts than with randomly paired content, indicating that organisational communication structures responses to topics. Six discourse types emerged, with critical and inquiry-driven engagement increasing over time. Post-level features did not significantly explain variation; organisational characteristics dominated. Larger housing associations attracted more substantive responses, while lower-rent organisations received fewer evaluative comments. While applied to housing associations, our methodology provides a scalable approach to analyse online discourse dynamics, quality, and content across organisations and contexts.
