In:
PLOS ONE, Public Library of Science (PLoS), Vol. 17, No. 1 ( 2022-1-26), p. e0261262-
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.
Type of Medium:
Online Resource
ISSN:
1932-6203
DOI:
10.1371/journal.pone.0261262
DOI:
10.1371/journal.pone.0261262.g001
DOI:
10.1371/journal.pone.0261262.g002
DOI:
10.1371/journal.pone.0261262.g003
DOI:
10.1371/journal.pone.0261262.g004
DOI:
10.1371/journal.pone.0261262.g005
DOI:
10.1371/journal.pone.0261262.g006
DOI:
10.1371/journal.pone.0261262.g007
DOI:
10.1371/journal.pone.0261262.t001
DOI:
10.1371/journal.pone.0261262.t002
DOI:
10.1371/journal.pone.0261262.t003
DOI:
10.1371/journal.pone.0261262.t004
DOI:
10.1371/journal.pone.0261262.s001
DOI:
10.1371/journal.pone.0261262.s002
Language:
English
Publisher:
Public Library of Science (PLoS)
Publication Date:
2022
detail.hit.zdb_id:
2267670-3
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