Skip to main content
Journal cover image

Uncovering cyberincivility among nurses and nursing students on Twitter: A data mining study.

Publication ,  Journal Article
De Gagne, JC; Hall, K; Conklin, JL; Yamane, SS; Wyman Roth, N; Chang, J; Kim, SS
Published in: International journal of nursing studies
January 2019

Although misuse of social networking sites, particularly Twitter, has occurred, little is known about the prevalence, content, and characteristics of uncivil tweets posted by nurses and nursing students.The aim of this study was to describe the characteristics of tweets posted by nurses and nursing students on Twitter with a focus on cyberincivility.A cross-sectional, data-mining study was held from February through April 2017. Using a data-mining tool, we extracted quantitative and qualitative data from a sample of 163 self-identified nurses and nursing students on Twitter. The analysis of 8934 tweets was performed by a combination of SAS 9.4 for descriptive and inferential statistics including logistic regression and NVivo 11 to derive descriptive patterns of unstructured textual data.We categorized 413 tweets (4.62%, n = 8934) as uncivil. Of these, 240 (58%) were related to nursing and the other 173 (42%) to personal life. Of the 163 unique users, 60 (36.8%) generated those 413 uncivil posts, tweeting inappropriately at least once over a period of six weeks. Most uncivil tweets contained profanity (n = 135, 32.7%), sexually explicit or suggestive material (n = 37, 9.0%), name-calling (n = 14, 3.4%), and discriminatory remarks against minorities (n = 9, 2.2%). Other uncivil content included product promotion, demeaning comments toward patients, aggression toward health professionals, and HIPAA violations.Nurses and nursing students share uncivil tweets that could tarnish the image of the profession and violate codes of ethics. Individual, interpersonal, and institutional efforts should be made to foster a culture of cybercivility.

Duke Scholars

Altmetric Attention Stats
Dimensions Citation Stats

Published In

International journal of nursing studies

DOI

EISSN

1873-491X

ISSN

0020-7489

Publication Date

January 2019

Volume

89

Start / End Page

24 / 31

Related Subject Headings

  • Students, Nursing
  • Social Media
  • Nursing
  • Nurses
  • Male
  • Interpersonal Relations
  • Humans
  • Female
  • Data Mining
  • Cyberbullying
 

Citation

APA
Chicago
ICMJE
MLA
NLM
De Gagne, J. C., Hall, K., Conklin, J. L., Yamane, S. S., Wyman Roth, N., Chang, J., & Kim, S. S. (2019). Uncovering cyberincivility among nurses and nursing students on Twitter: A data mining study. International Journal of Nursing Studies, 89, 24–31. https://doi.org/10.1016/j.ijnurstu.2018.09.009
De Gagne, Jennie C., Katherine Hall, Jamie L. Conklin, Sandra S. Yamane, Noelle Wyman Roth, Jianhong Chang, and Sang Suk Kim. “Uncovering cyberincivility among nurses and nursing students on Twitter: A data mining study.International Journal of Nursing Studies 89 (January 2019): 24–31. https://doi.org/10.1016/j.ijnurstu.2018.09.009.
De Gagne JC, Hall K, Conklin JL, Yamane SS, Wyman Roth N, Chang J, et al. Uncovering cyberincivility among nurses and nursing students on Twitter: A data mining study. International journal of nursing studies. 2019 Jan;89:24–31.
De Gagne, Jennie C., et al. “Uncovering cyberincivility among nurses and nursing students on Twitter: A data mining study.International Journal of Nursing Studies, vol. 89, Jan. 2019, pp. 24–31. Epmc, doi:10.1016/j.ijnurstu.2018.09.009.
De Gagne JC, Hall K, Conklin JL, Yamane SS, Wyman Roth N, Chang J, Kim SS. Uncovering cyberincivility among nurses and nursing students on Twitter: A data mining study. International journal of nursing studies. 2019 Jan;89:24–31.
Journal cover image

Published In

International journal of nursing studies

DOI

EISSN

1873-491X

ISSN

0020-7489

Publication Date

January 2019

Volume

89

Start / End Page

24 / 31

Related Subject Headings

  • Students, Nursing
  • Social Media
  • Nursing
  • Nurses
  • Male
  • Interpersonal Relations
  • Humans
  • Female
  • Data Mining
  • Cyberbullying