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 n...
| Publicado en: | Journal of Advanced Nursing (John Wiley & Sons, Inc.) Vol. 81; no. 12; pp. 8724 - 8738 |
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| Autores principales: | , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
John Wiley & Sons, Inc.
Dec2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=192267684&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 192267684 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 03092402 LVDY jtl: Journal of Advanced Nursing (John Wiley & Sons, Inc.) issn: 03092402 maglogo: N pubinfo: dt: Dec2025 vid: 81 iid: 12 pid: 52269 pub: John Wiley & Sons, Inc. artinfo: ui: 192267684 183800027 192267684 192267684 10.1111/jan.16847 192267684 ppf: 8724 ppct: 14 formats: tig: atl: Workplace Violence Against Chinese Nurses From the Perspectives of Social Media and News Reports: A Multilayer Text Mining Analysis. aug: au: Cao, Yucheng Gao, Yu Chappell, Kathy affil: Johns Hopkins Bloomberg School of Public Health, Baltimore Maryland,, USA sug: subj: Workplace Violence Evaluation Data Mining Social Media News Nursing Staff, Hospital Psychosocial Factors Chinese Persons Psychosocial Factors China Human Male Female Quantitative Studies Empirical Research Sentiment Analysis Social Network Analysis Emotions Bullying Interpersonal Relations Male Female ab: 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. Patient or Public Contribution: Not applicable. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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