Emojis predict dropouts of remote workers: An empirical study of emoji usage on GitHub
It addresses the challenge of monitoring remote worker emotions and predicting attrition for organizations using online platforms, though it is incremental as it applies existing methods to a new dataset.
This paper tackles the problem of predicting remote worker dropouts by analyzing emoji usage on GitHub, finding that developers who use emojis are significantly less likely to drop out and that machine learning models using emoji features can predict dropouts with satisfactory accuracy.
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.