{"title":"社交媒体与新闻报道视角下的中国护士职场暴力：多层文本挖掘分析","authors":"Yucheng Cao, Yu Gao, Kathy Chappell","doi":"10.1111/jan.16847","DOIUrl":null,"url":null,"abstract":"<div>\n \n \n <section>\n \n <h3> Aims</h3>\n \n <p>This study compares the emotional expressions and structural characteristics of workplace violence (WPV) against Chinese nurses in social media comments and news reports, highlighting differences and focal points in dissemination.</p>\n </section>\n \n <section>\n \n <h3> Design</h3>\n \n <p>A quantitative study utilising text mining and social network analysis.</p>\n </section>\n \n <section>\n \n <h3> Methods</h3>\n \n <p>Data containing the keywords ‘nurse violence’, ‘nurse workplace violence’, ‘nurse bullying’, ‘nurse workplace bullying’ were collected from social media platforms (e.g., Xiaohongshu, Zhihu, Weibo) and news platforms (e.g., Baidu News, People's Daily, Xinhua News) between January 1, 2016, and October 31, 2024. Using Python 3.8.9, time trends and sentiment analyses were performed, while Ucinet 6.0 was used for social network analysis to explore dissemination patterns and keyword structures. A total of 5431 social media comments and 89 news reports were analysed.</p>\n </section>\n \n <section>\n \n <h3> Results</h3>\n \n <p>Temporal analysis showed that social media attention to WPV against nurses significantly exceeded that of news reports, with a peak in 2024. Sentiment analysis revealed predominantly negative emotions (52.75%) on social media, while news reports exhibited a more positive tone (62.92%). Social network analysis revealed stark differences in keyword structures between platforms. Social media exhibited a dense and decentralised network, with keywords like ‘head nurse’, ‘leader’ and ‘bullying’ highlighting internal professional conflicts. In contrast, news reports showed a centralised network focusing on external violent incidents, with keywords such as ‘violence’, ‘assault’ and ‘patient’ dominating.</p>\n </section>\n \n <section>\n \n <h3> Conclusions</h3>\n \n <p>Social media and news reports demonstrated significant differences in describing WPV against nurses. Social media focused on emotional expressions of interpersonal conflicts, whereas news reports prioritised factual accounts of violent incidents and proposed solutions.</p>\n </section>\n \n <section>\n \n <h3> Impact</h3>\n \n <p>This study offers insights into how WPV against nurses is communicated through different media, helping nursing administrators and policymakers understand the complexity of these narratives. The findings can inform the development of targeted communication strategies to address WPV and enhance public awareness.</p>\n </section>\n \n <section>\n \n <h3> Patient or Public Contribution</h3>\n \n <p>Not applicable.</p>\n </section>\n </div>","PeriodicalId":54897,"journal":{"name":"Journal of Advanced Nursing","volume":"81 12","pages":"8724-8737"},"PeriodicalIF":3.8000,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Workplace Violence Against Chinese Nurses From the Perspectives of Social Media and News Reports: A Multilayer Text Mining Analysis\",\"authors\":\"Yucheng Cao, Yu Gao, Kathy Chappell\",\"doi\":\"10.1111/jan.16847\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n \\n \\n <section>\\n \\n <h3> Aims</h3>\\n \\n <p>This study compares the emotional expressions and structural characteristics of workplace violence (WPV) against Chinese nurses in social media comments and news reports, highlighting differences and focal points in dissemination.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Design</h3>\\n \\n <p>A quantitative study utilising text mining and social network analysis.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Methods</h3>\\n \\n <p>Data containing the keywords ‘nurse violence’, ‘nurse workplace violence’, ‘nurse bullying’, ‘nurse workplace bullying’ were collected from social media platforms (e.g., Xiaohongshu, Zhihu, Weibo) and news platforms (e.g., Baidu News, People's Daily, Xinhua News) between January 1, 2016, and October 31, 2024. Using Python 3.8.9, time trends and sentiment analyses were performed, while Ucinet 6.0 was used for social network analysis to explore dissemination patterns and keyword structures. A total of 5431 social media comments and 89 news reports were analysed.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Results</h3>\\n \\n <p>Temporal analysis showed that social media attention to WPV against nurses significantly exceeded that of news reports, with a peak in 2024. Sentiment analysis revealed predominantly negative emotions (52.75%) on social media, while news reports exhibited a more positive tone (62.92%). Social network analysis revealed stark differences in keyword structures between platforms. Social media exhibited a dense and decentralised network, with keywords like ‘head nurse’, ‘leader’ and ‘bullying’ highlighting internal professional conflicts. In contrast, news reports showed a centralised network focusing on external violent incidents, with keywords such as ‘violence’, ‘assault’ and ‘patient’ dominating.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Conclusions</h3>\\n \\n <p>Social media and news reports demonstrated significant differences in describing WPV against nurses. Social media focused on emotional expressions of interpersonal conflicts, whereas news reports prioritised factual accounts of violent incidents and proposed solutions.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Impact</h3>\\n \\n <p>This study offers insights into how WPV against nurses is communicated through different media, helping nursing administrators and policymakers understand the complexity of these narratives. The findings can inform the development of targeted communication strategies to address WPV and enhance public awareness.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Patient or Public Contribution</h3>\\n \\n <p>Not applicable.</p>\\n </section>\\n </div>\",\"PeriodicalId\":54897,\"journal\":{\"name\":\"Journal of Advanced Nursing\",\"volume\":\"81 12\",\"pages\":\"8724-8737\"},\"PeriodicalIF\":3.8000,\"publicationDate\":\"2025-02-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Advanced Nursing\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1111/jan.16847\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"NURSING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Advanced Nursing","FirstCategoryId":"3","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1111/jan.16847","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"NURSING","Score":null,"Total":0}
Workplace Violence Against Chinese Nurses From the Perspectives of Social Media and News Reports: A Multilayer Text Mining Analysis
Aims
This study compares the emotional expressions and structural characteristics of workplace violence (WPV) against Chinese nurses in social media comments and news reports, highlighting differences and focal points in dissemination.
Design
A quantitative study utilising text mining and social network analysis.
Methods
Data containing the keywords ‘nurse violence’, ‘nurse workplace violence’, ‘nurse bullying’, ‘nurse workplace bullying’ were collected from social media platforms (e.g., Xiaohongshu, Zhihu, Weibo) and news platforms (e.g., Baidu News, People's Daily, Xinhua News) between January 1, 2016, and October 31, 2024. Using Python 3.8.9, time trends and sentiment analyses were performed, while Ucinet 6.0 was used for social network analysis to explore dissemination patterns and keyword structures. A total of 5431 social media comments and 89 news reports were analysed.
Results
Temporal analysis showed that social media attention to WPV against nurses significantly exceeded that of news reports, with a peak in 2024. Sentiment analysis revealed predominantly negative emotions (52.75%) on social media, while news reports exhibited a more positive tone (62.92%). Social network analysis revealed stark differences in keyword structures between platforms. Social media exhibited a dense and decentralised network, with keywords like ‘head nurse’, ‘leader’ and ‘bullying’ highlighting internal professional conflicts. In contrast, news reports showed a centralised network focusing on external violent incidents, with keywords such as ‘violence’, ‘assault’ and ‘patient’ dominating.
Conclusions
Social media and news reports demonstrated significant differences in describing WPV against nurses. Social media focused on emotional expressions of interpersonal conflicts, whereas news reports prioritised factual accounts of violent incidents and proposed solutions.
Impact
This study offers insights into how WPV against nurses is communicated through different media, helping nursing administrators and policymakers understand the complexity of these narratives. The findings can inform the development of targeted communication strategies to address WPV and enhance public awareness.
期刊介绍：
The Journal of Advanced Nursing (JAN) contributes to the advancement of evidence-based nursing, midwifery and healthcare by disseminating high quality research and scholarship of contemporary relevance and with potential to advance knowledge for practice, education, management or policy.
All JAN papers are required to have a sound scientific, evidential, theoretical or philosophical base and to be critical, questioning and scholarly in approach. As an international journal, JAN promotes diversity of research and scholarship in terms of culture, paradigm and healthcare context. For JAN’s worldwide readership, authors are expected to make clear the wider international relevance of their work and to demonstrate sensitivity to cultural considerations and differences.
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
