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

Abstract

Background: 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. Objective: 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. Method: 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. Findings: 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. Conclusion: 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.