Paper Number

3034

Paper Type

Short

Abstract

AI tools are widely used in open collaboration communities nowadays, yet factors influencing their adoption are not well understood. This study investigates the factors influencing bot approval on English Wikipedia. Leveraging the advanced capabilities of Large Language Models (LLMs) on NLP tasks, we systematically extract humancomputer interaction (HCI) factors from 2,155 Wikipedia Bot Request for Approval pages. We identify ten themes that emerged from community discussion concerning three main aspects: Technological Factors, User Interaction and Experience, and Platform Governance. We further assess the comparative importance of each theme in the community's bot approval decisions. Our findings reveal that Technological Factors play the most significant role, followed by User Interaction and Experience and Platform Governance Standards. This study contributes quantitative evidence to understanding bot approval in open collaboration communities and proposes a novel, generalizable LLM-based approach for extracting and summarizing themes from large text corpora.

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09-HTI

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Dec 15th, 12:00 AM

To Automate or Not? How Open Collaboration Communities Decide on Bot Adoption

AI tools are widely used in open collaboration communities nowadays, yet factors influencing their adoption are not well understood. This study investigates the factors influencing bot approval on English Wikipedia. Leveraging the advanced capabilities of Large Language Models (LLMs) on NLP tasks, we systematically extract humancomputer interaction (HCI) factors from 2,155 Wikipedia Bot Request for Approval pages. We identify ten themes that emerged from community discussion concerning three main aspects: Technological Factors, User Interaction and Experience, and Platform Governance. We further assess the comparative importance of each theme in the community's bot approval decisions. Our findings reveal that Technological Factors play the most significant role, followed by User Interaction and Experience and Platform Governance Standards. This study contributes quantitative evidence to understanding bot approval in open collaboration communities and proposes a novel, generalizable LLM-based approach for extracting and summarizing themes from large text corpora.

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