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Emojis predict dropouts of remote workers: An empirical study of emoji usage on GitHub
- Source :
- PLoS ONE, PLoS ONE, Vol 17, Iss 1, p e0261262 (2022), PLoS ONE, Vol 17, Iss 1 (2022)
- Publication Year :
- 2022
- Publisher :
- Public Library of Science (PLoS), 2022.
-
Abstract
- Emotions at work have long been identified as critical signals of work motivations, status, and attitudes, and as predictors of various work-related outcomes. When more and more employees work remotely, these emotional signals of workers become harder to observe through daily, face-to-face communications. The use of online platforms to communicate and collaborate at work provides an alternative channel to monitor the emotions of workers. This paper studies how emojis, as non-verbal cues in online communications, can be used for such purposes and how the emotional signals in emoji usage can be used to predict future behavior of workers. In particular, we present how the developers on GitHub use emojis in their work-related activities. We show that developers have diverse patterns of emoji usage, which can be related to their working status including activity levels, types of work, types of communications, time management, and other behavioral patterns. Developers who use emojis in their posts are significantly less likely to dropout from the online work platform. Surprisingly, solely using emoji usage as features, standard machine learning models can predict future dropouts of developers at a satisfactory accuracy. Features related to the general use and the emotions of emojis appear to be important factors, while they do not rule out paths through other purposes of emoji use.
- Subjects :
- FOS: Computer and information sciences
Computer Science - Machine Learning
Computer and Information Sciences
Science
Entropy
Emotions
Twitter
Social Sciences
Psychological Stress
Machine Learning (cs.LG)
Machine Learning
Sociology
Artificial Intelligence
Mental Health and Psychiatry
Medicine and Health Sciences
Humans
Psychology
Facial Expressions
Nonverbal Communication
Behavior
Multidisciplinary
Communication
Physics
Teleworking
Biology and Life Sciences
Software Engineering
Social Communication
Communications
Facial Expression
Social Networks
Physical Sciences
Medicine
Engineering and Technology
Thermodynamics
Programming Languages
Social Media
Network Analysis
Research Article
Subjects
Details
- ISSN :
- 19326203
- Volume :
- 17
- Database :
- OpenAIRE
- Journal :
- PLOS ONE
- Accession number :
- edsair.doi.dedup.....13121d5cad8538f5fe03e4a5ad5e6a38