Does online sustainability communication shape public discourse? Insights from six years of tenant-housing provider interactions

2026-07-09

AI Summary

This study addresses the limitations of existing research that relies heavily on aggregated metrics such as likes, which fail to capture the structure and quality of public discourse regarding institutional sustainability communication. To overcome this, the authors propose a multidimensional, data-driven framework integrating machine learning classification, semantic relatedness analysis, and multinomial logistic regression to systematically examine 792 Facebook posts and 3,197 associated comments published by Dutch public housing organizations between 2018 and 2023. The analysis reveals six dynamically evolving discourse types, demonstrating that tenant comments are semantically aligned with post content and that critical and inquiry-based interactions have significantly increased over time. Organizational size and rent levels are found to substantially influence discourse depth and evaluativeness, whereas post design exhibits limited impact. This framework offers a scalable, novel approach to understanding how organizational communication structurally shapes public discourse.

Abstract

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.

Problem

• online sustainability communication
• public discourse
• social media engagement
• discourse quality
• citizen response

Innovation

• machine learning
• discourse analysis
• social media
• sustainability communication
• multidimensional framework