Emotional Contagion in Code: How GitHub Emoji Reactions Shape Developer Collaboration
Obada Kraishan
TL;DR
The study investigates how GitHub emoji reactions spread emotions in technical discussions and influence collaboration. Using data from 2,098 issues and PRs across 50 repositories, it applies a weighted sentiment framework with $S_i$ and $\bar{S}_i$, identifies five emotional patterns (notably Instant Enthusiasm), and tests links to outcomes via non-parametric statistics and a random forest predictor. Key findings show strong emotional contagion ($r=0.679$, $p<0.001$, $d=2.393$), a prevalence of positive signals (e.g., 91% positivity in Instant Enthusiasm), and higher PR merge rates and contributor retention associated with positive emotional climates. The work demonstrates that minimal emotional cues in asynchronous discussions meaningfully shape discussion trajectories and project dynamics, with practical implications for platform design, moderation, and recognizing emotional labor. It also highlights limitations in causality, cultural generalizability, and the influence of bots, suggesting further cross-platform and longitudinal research including non-Western communities and AI-driven contributors.
Abstract
Developer communities increasingly rely on emoji reactions to communicate, but we know little about how these emotional signals spread and influence technical discussions. We analyzed 2,098 GitHub issues and pull requests across 50 popular repositories, examining patterns in 106,743 emoji reactions to understand emotional contagion in software development. Our findings reveal a surprisingly positive emotional landscape: 57.4% of discussions carry positive sentiment, with positive emotional cascades outnumbering negative ones 23:1. We identified five distinct patterns, with "instant enthusiasm" affecting 45.6% of items--nearly half receive immediate positive reinforcement. Statistical analysis confirms strong emotional contagion (r=0.679, p<0.001) with a massive effect size (d=2.393), suggesting that initial reactions powerfully shape discussion trajectories. These findings challenge assumptions about technical discourse being purely rational, demonstrating that even minimal emotional signals create measurable ripple effects. Our work provides empirical evidence that emoji reactions are not mere decoration but active forces shaping collaborative outcomes in software development.