🤖 AI Summary
This study investigates whether emoji reactions on GitHub drive emotional contagion and influence developer collaboration outcomes. Method: Analyzing 106,743 emoji reactions across 2,098 issues/pull requests, we employed sentiment polarity classification, social network cascade analysis, effect-size estimation (Cohen’s *d*), and correlation analysis. Contribution/Results: We identify significant positive emotional contagion in technical discussions (*r* = 0.679, *d* = 2.393), with a positive-to-negative emotional cascade ratio of 23:1; 57.4% of discussions exhibit dominant positive sentiment. We propose and empirically validate five distinct emoji-driven emotional propagation patterns, demonstrating that initial affective feedback strongly steers discussion trajectories. Findings establish emoji not merely as expressive tokens but as critical affective signaling mechanisms shaping collaborative dynamics. This work provides empirical grounding and methodological insights for human-AI collaboration and affective computing in open-source communities.
📝 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.