Salvatore Camilla, Biffignandi Silvia, Bianchi Annamaria
Department of Economics, Management and Statistics (DEMS), University of Milano-Bicocca, Piazza dell'Ateneo Nuovo, 1, 20126 Milan, Italy.
Bergamo, Italy.
Soc Indic Res. 2022;164(3):1217-1248. doi: 10.1007/s11205-022-02993-8. Epub 2022 Aug 20.
The communication of corporate social responsibility (CSR) highlights the behavior of the business toward CSR and their framework of sustainable development (SD), thus helping policymakers understand the role businesses play with respect to the 2030 Agenda. Despite its importance, this is still a relatively underexamined and emerging topic. In our paper, we focus on what businesses communicate about CSR through social media and how this relates to the Sustainable Development Goals (SDGs). We identified the topics discussed on Twitter, their evolution over time, and the differences across sectors. We applied the structural topic model (STM) algorithm, which allowed us to estimate the model, including document-level metadata (time and sector). This model proved to be a powerful tool for topic detection and the estimation of the effects of time and sector on the discussion proportion of the topics. Indeed, we found that the topics were well identified overall, and the model allowed catching signals from the data. We derived CSR communication indexes directly from the topic model (TM) results and propose the use of dissimilarity and homogeneity indexes to describe the communication mix and highlight differences and identify clusters.
企业社会责任（CSR）的传播突出了企业在企业社会责任方面的行为及其可持续发展（SD）框架，从而有助于政策制定者理解企业在2030年议程中所扮演的角色。尽管其重要性，但这仍是一个相对未得到充分研究的新兴话题。在我们的论文中，我们关注企业通过社交媒体传播的有关企业社会责任的内容，以及这与可持续发展目标（SDGs）的关系。我们确定了在推特上讨论的话题、它们随时间的演变以及各部门之间的差异。我们应用了结构主题模型（STM）算法，该算法使我们能够估计模型，包括文档级元数据（时间和部门）。事实证明，该模型是用于主题检测以及估计时间和部门对主题讨论比例影响的强大工具。的确，我们发现总体上主题得到了很好的识别，并且该模型能够从数据中捕捉信号。我们直接从主题模型（TM）结果中得出企业社会责任传播指数，并建议使用差异指数和同质性指数来描述传播组合，突出差异并识别集群。
