{"title":"Dialogic analysis of government social media communication: How commanding and thanking elicit blame","authors":"Ruth Page , Sten Hansson","doi":"10.1016/j.dcm.2024.100757","DOIUrl":null,"url":null,"abstract":"<div><p>During major crises, such as the Covid-19 pandemic, government officeholders issue commands to change people’s behaviour (e.g., ‘Stay at home!’) and express thanks to acknowledge the efforts of others and build solidarity. We use specialised datasets of replies to social media posts by government ministers in the United Kingdom during Covid-19 lockdowns to explore how people react to their messages that contain directive speech acts and thanking. Empirically, our corpus-assisted analysis of evaluative language and blaming shows that far from promoting team spirit, thanking may elicit at least as much, if not more blaming language than commands. Methodologically, we demonstrate how to analyse government social media communication dialogically to gain more nuanced insights about online feedback from citizens.</p></div>","PeriodicalId":46649,"journal":{"name":"Discourse Context & Media","volume":"57 ","pages":"Article 100757"},"PeriodicalIF":2.3000,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2211695824000035/pdfft?md5=01e24e09b5827bb488fbe78867972b68&pid=1-s2.0-S2211695824000035-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Discourse Context & Media","FirstCategoryId":"98","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2211695824000035","RegionNum":2,"RegionCategory":"文学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMMUNICATION","Score":null,"Total":0}
引用次数: 0
Abstract
During major crises, such as the Covid-19 pandemic, government officeholders issue commands to change people’s behaviour (e.g., ‘Stay at home!’) and express thanks to acknowledge the efforts of others and build solidarity. We use specialised datasets of replies to social media posts by government ministers in the United Kingdom during Covid-19 lockdowns to explore how people react to their messages that contain directive speech acts and thanking. Empirically, our corpus-assisted analysis of evaluative language and blaming shows that far from promoting team spirit, thanking may elicit at least as much, if not more blaming language than commands. Methodologically, we demonstrate how to analyse government social media communication dialogically to gain more nuanced insights about online feedback from citizens.
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