The Double-edged Effect of Banning Generative AI on Online Question-and-Answer Communities: Evidence from Stack Exchange

2026-07-05

AI Summary

This study investigates the impact of generative artificial intelligence–generated content (AIGC) bans in online question-and-answer communities on knowledge-seeking and contribution behaviors. Leveraging site-wide data from Stack Exchange, the authors employ a difference-in-differences (DID) approach to analyze community dynamics before and after the implementation of AIGC restrictions, integrating a sociotechnical theoretical framework to examine underlying mechanisms. The findings reveal that in non-STEM communities, the ban significantly increases question volume but reduces answer efficiency and the likelihood of receiving satisfactory answers promptly. The study further uncovers that perceived information reliability and social interactivity are key mechanisms driving these behavioral shifts, thereby highlighting the dual-edged nature of AIGC prohibition policies.

Abstract

We investigate how banning generative artificial intelligence-generated content (AIGC) affects knowledge seeking, knowledge contribution, and contribution efficiency in online question-and-answer communities. After the launch of ChatGPT in late November 2022, several Stack Exchange communities implemented official bans on AIGC over concerns such as less reliable and socially engaged content. Leveraging data from the full network of Stack Exchange communities, we employ a difference-in-differences (DID) approach to examine the impacts of these bans. Our results reveal a double-edged impact: while the AIGC ban increases knowledge seeking, as evidenced by a higher volume of posted questions, it simultaneously reduces contribution efficiency, reflected in a lower proportion of questions receiving satisfactory answers within the expected time frame. Notably, these impacts are only evident in non-STEM communities. We take a socio-technical perspective to explore information reliability and social interactivity as two plausible underlying factors driving the observed changes. Our mechanism exploration reveals that the AIGC ban spurs question volume in topics where AIGC is less reliable and where social interaction is highly expected. In contrast, the ban hampers answer efficiency in communities where LLMs are capable of producing reliable answers and where social interactivity is minimal. Additionally, our results indicate the increased human involvement from knowledge seekers and contributors following the ban. They adapt their behavior by posting questions and answers that are more informationally rich and socially engaging. Overall, our findings offer actionable implications for platform managers, community moderators, and policymakers of online Q&A communities.

Problem

• generative AI
• online Q&A communities
• AIGC ban
• knowledge contribution
• contribution efficiency

Innovation

• generative AI ban
• online Q&A communities
• difference-in-differences
• contribution efficiency
• socio-technical perspective