在线可持续性沟通能否塑造公共话语？来自六年租户与住房供应商互动的见解
Does online sustainability communication shape public discourse? Insights from six years of tenant-housing provider interactions
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中文总结 AI 辅助
研究当局借助社交媒体推动可持续性相关事宜时公民的反应，开发数据驱动多维框架，分析荷兰公共住房中社交媒体沟通对话语内容的塑造，通过机器学习等方法得出相关结论，提供了可跨组织和背景分析在线话语的可扩展方法。
中文摘要 AI 辅助
当局越来越依赖社交媒体来推动可持续性转型、基础设施投资和服务改革，但公民对这些数字通信的反应仍知之甚少。现有方法依赖于总体参与指标，对话语结构和质量洞察有限。我们开发了一个数据驱动的多维框架，以分析社交媒体沟通如何塑造话语内容，重点关注荷兰公共住房中与可持续性相关的参与。我们分析了92个住房供应商（2018 - 2023年）Facebook页面上的792篇帖子和3197条租户评论。通过机器学习管道将评论分类为跨交际意图、情感和语义相关性三个维度的反复出现的话语配置。多项逻辑回归估计了帖子设计和组织特征对话语的影响。租户评论在语义上与相应帖子的一致性明显高于与随机配对内容的一致性，表明组织沟通构建了对主题的回应。出现了六种话语类型，批判性和探究性参与随时间增加。帖子层面的特征不能显著解释变化，组织特征起主导作用。较大的住房协会吸引了更多实质性回应，而低租金组织收到的评价性评论较少。虽然该方法应用于住房协会，但它提供了一种可扩展的方法来分析跨组织和背景的在线话语动态、质量和内容。
英文摘要
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.
