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Emoji Promotes Developer Participation and Issue Resolution on GitHub

Yuhang Zhou, Xuan Lu, Ge Gao, Qiaozhu Mei, Wei Ai

TL;DR

This study investigates whether emoji usage on GitHub issues causally affects developer participation and issue resolution in a remote-work setting. It uses a large-scale dataset from GHTorrent (over 200k issues, including 14.7k emoji-containing issues) and applies propensity score matching with rich confounder controls (textual features, topic modeling, author and repository metrics) to estimate causal effects. The results show that emojis significantly increase participation and speed up resolution, with effects varying by issue type and stronger signals when emojis align with issue semantics; sensitivity analyses support robustness to unobserved confounders. The findings offer practical guidance for emoji use to improve communication efficiency in distributed software teams while acknowledging limitations and the need for broader validation across languages and contexts.

Abstract

Although remote working is increasingly adopted during the pandemic, many are concerned by the low-efficiency in the remote working. Missing in text-based communication are non-verbal cues such as facial expressions and body language, which hinders the effective communication and negatively impacts the work outcomes. Prevalent on social media platforms, emojis, as alternative non-verbal cues, are gaining popularity in the virtual workspaces well. In this paper, we study how emoji usage influences developer participation and issue resolution in virtual workspaces. To this end, we collect GitHub issues for a one-year period and apply causal inference techniques to measure the causal effect of emojis on the outcome of issues, controlling for confounders such as issue content, repository, and author information. We find that emojis can significantly reduce the resolution time of issues and attract more user participation. We also compare the heterogeneous effect on different types of issues. These findings deepen our understanding of the developer communities, and they provide design implications on how to facilitate interactions and broaden developer participation.

Emoji Promotes Developer Participation and Issue Resolution on GitHub

TL;DR

This study investigates whether emoji usage on GitHub issues causally affects developer participation and issue resolution in a remote-work setting. It uses a large-scale dataset from GHTorrent (over 200k issues, including 14.7k emoji-containing issues) and applies propensity score matching with rich confounder controls (textual features, topic modeling, author and repository metrics) to estimate causal effects. The results show that emojis significantly increase participation and speed up resolution, with effects varying by issue type and stronger signals when emojis align with issue semantics; sensitivity analyses support robustness to unobserved confounders. The findings offer practical guidance for emoji use to improve communication efficiency in distributed software teams while acknowledging limitations and the need for broader validation across languages and contexts.

Abstract

Although remote working is increasingly adopted during the pandemic, many are concerned by the low-efficiency in the remote working. Missing in text-based communication are non-verbal cues such as facial expressions and body language, which hinders the effective communication and negatively impacts the work outcomes. Prevalent on social media platforms, emojis, as alternative non-verbal cues, are gaining popularity in the virtual workspaces well. In this paper, we study how emoji usage influences developer participation and issue resolution in virtual workspaces. To this end, we collect GitHub issues for a one-year period and apply causal inference techniques to measure the causal effect of emojis on the outcome of issues, controlling for confounders such as issue content, repository, and author information. We find that emojis can significantly reduce the resolution time of issues and attract more user participation. We also compare the heterogeneous effect on different types of issues. These findings deepen our understanding of the developer communities, and they provide design implications on how to facilitate interactions and broaden developer participation.
Paper Structure (32 sections, 4 equations, 5 figures, 11 tables)

This paper contains 32 sections, 4 equations, 5 figures, 11 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Emoji Functionalities
  4. User Engagement in GitHub Issues
  5. GitHub Issues and Problem Formulation
  6. Data Collection
  7. Outcome Variables
  8. Propensity Score Matching
  9. Collection of Confounders
  10. Issue Text Confounders
  11. Issue Author and Repository Confounders
  12. Causal Inference of Emojis on Issues
  13. Propensity Score Estimation
  14. Nearest Neighbor Matching
  15. Results and Discussion
  16. ...and 17 more sections

Figures (5)

  • Figure 1: An example of emoji usage in GitHub Issues. The author puts (lady beetle) in the issue title and (face with monocle) in the issue body.
  • Figure 2: Causal graph for the backdoor adjustment
  • Figure 3: The example of an issue with self-annotated labels
  • Figure 4: An issue template for feature request in the microsoft/PowerToys repository
  • Figure 5: SMD value before matching and after matching for each covariate. After the matching process, the SMD value of all covariates are smaller than 0.1, indicating the similar distribution of all covariates between treatment and control group.