Emojis predict dropouts of remote workers: An empirical study of emoji usage on GitHub
Xuan Lu, Wei Ai, Zhenpeng Chen, Yanbin Cao, Qiaozhu Mei

TL;DR
This study demonstrates that emoji usage in online work communications, particularly on GitHub, can serve as an emotional signal to predict developer dropout, with emoji features enabling effective machine learning predictions.
Contribution
It introduces a novel approach to monitor emotional signals through emoji usage in online work environments and shows their predictive power for developer dropout.
Findings
Developers exhibit diverse emoji usage patterns related to their work behavior.
Emoji usage significantly correlates with developers' likelihood to dropout.
Machine learning models using emoji features predict dropouts with satisfactory accuracy.
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,…
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Taxonomy
MethodsDropout
