Patterns of Bot Participation and Emotional Influence in Open-Source Development

2026-01-16

AI Summary

This study investigates the impact of bot participation on developers’ emotional expression in open-source software development. Leveraging interaction data from 36,875 accounts in the Ethereum ecosystem, the authors identify 105 active bots and apply a sentiment analysis model encompassing 27 emotion categories to quantitatively examine bot engagement patterns in issues and pull requests, as well as their influence on human emotions. The research reveals, for the first time, that even a small number of bots can significantly alter the temporal rhythm and emotional dynamics of community communication. Although bots exhibit predominantly neutral behavior, their involvement correlates with a measurable decrease in neutrality and a significant increase in positive emotions—such as gratitude, admiration, and optimism—in subsequent human comments, thereby demonstrating bots’ regulatory role in shaping the affective climate of open-source communities.

Technology Category

• Humans and AI: Emotional Intelligence
• Intelligent Robots: Human-Robot Interaction
• Cognitive Modeling & Cognitive Systems: Affective Computing

Application Category

• Web Mining and Content Analysis: Sentiment analysis and opinion mining
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

We study how bots contribute to open-source discussions in the Ethereum ecosystem and whether they influence developers'emotional tone. Our dataset covers 36,875 accounts across ten repositories with 105 validated bots (0.28%). Human participation follows a U-shaped pattern, while bots engage in uniform (pull requests) or late-stage (issues) activity. Bots respond faster than humans in pull requests but play slower maintenance roles in issues. Using a model trained on 27 emotion categories, we find bots are more neutral, yet their interventions are followed by reduced neutrality in human comments, with shifts toward gratitude, admiration, and optimism and away from confusion. These findings indicate that even a small number of bots are associated with changes in both timing and emotional dynamics of developer communication.

Problem

• bot participation
• emotional influence
• open-source development
• developer communication
• Ethereum ecosystem

Innovation

• bot participation
• emotional influence
• open-source development
• developer communication
• computational social science