{"title":"Exploring business and SDG discourse on X: topics, users and engagement","authors":"Christine Ascencio, Randika Eramudugoda","doi":"10.1108/ccij-10-2023-0143","DOIUrl":null,"url":null,"abstract":"PurposeThis paper examines thematic discourses concerning business and the Sustainable Development Goals (SDGs) on X (formerly Twitter), aiming to uncover active user groups and evaluate engagement levels across various topics. The study also explores the engagement patterns among different user categories, ultimately seeking deeper insights into platform discourse regarding business and the SDGs.Design/methodology/approachUtilizing unsupervised machine learning technique Latent Dirichlet Allocation (LDA), we perform exploratory topic modeling on X data referencing business and the SDGs, generating 16 thematic clusters. Subsequently, we analyze user descriptions to categorize users involved in these discussions. Finally, we employ binomial logit models to assess the relationship between topics and engagement and chi-squared test to evaluate the relationship between users and topics.FindingsThe exploratory research identifies 16 business and SDG topics, while the analysis of users reveals 6 stakeholder groups contributing to these discussions. Business groups emerge as the most frequent contributors, posting on topics related to partnership, action advocacy, and economic outcomes. Topics about updates on progress and transformative initiatives garnered strongest support for engagement.Originality/valueThis research not only sheds light on the current state of business and SDG discourse on X, but also underscores the significance of engaging external stakeholders in driving positive social change globally.","PeriodicalId":10696,"journal":{"name":"Corporate Communications: An International Journal","volume":"82 17","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Corporate Communications: An International Journal","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1108/ccij-10-2023-0143","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
PurposeThis paper examines thematic discourses concerning business and the Sustainable Development Goals (SDGs) on X (formerly Twitter), aiming to uncover active user groups and evaluate engagement levels across various topics. The study also explores the engagement patterns among different user categories, ultimately seeking deeper insights into platform discourse regarding business and the SDGs.Design/methodology/approachUtilizing unsupervised machine learning technique Latent Dirichlet Allocation (LDA), we perform exploratory topic modeling on X data referencing business and the SDGs, generating 16 thematic clusters. Subsequently, we analyze user descriptions to categorize users involved in these discussions. Finally, we employ binomial logit models to assess the relationship between topics and engagement and chi-squared test to evaluate the relationship between users and topics.FindingsThe exploratory research identifies 16 business and SDG topics, while the analysis of users reveals 6 stakeholder groups contributing to these discussions. Business groups emerge as the most frequent contributors, posting on topics related to partnership, action advocacy, and economic outcomes. Topics about updates on progress and transformative initiatives garnered strongest support for engagement.Originality/valueThis research not only sheds light on the current state of business and SDG discourse on X, but also underscores the significance of engaging external stakeholders in driving positive social change globally.
目的本文研究了 X(原 Twitter)上有关商业和可持续发展目标(SDGs)的主题讨论,旨在发现活跃的用户群体并评估不同主题的参与程度。本研究还探讨了不同用户类别之间的参与模式,最终寻求对有关商业和可持续发展目标的平台讨论的更深入的见解。设计/方法/途径利用无监督机器学习技术潜狄利克特分配(LDA),我们对 X 数据中有关商业和可持续发展目标的内容进行了探索性主题建模,生成了 16 个主题聚类。随后,我们分析用户描述,对参与这些讨论的用户进行分类。最后,我们采用二叉对数模型评估主题与参与度之间的关系,并采用卡方检验评估用户与主题之间的关系。商业团体是最常参与讨论的群体,他们发布的话题涉及伙伴关系、行动倡导和经济成果。这项研究不仅揭示了 X 上商业和可持续发展目标讨论的现状,还强调了外部利益相关者参与推动全球积极社会变革的重要意义。