The Decline of Online Knowledge Communities: Obstacles, Workarounds, and Sustainability

TL;DR

The paper examines how online knowledge communities adapt to the rise of Generative AI, highlighting challenges and opportunities for sustaining community engagement and trust amidst systemic shifts.

Contribution

It provides empirical insights into user attitudes and community dynamics in the face of AI-driven changes, proposing design strategies for resilience.

Findings

Users rely on AI for convenience but prefer OKC for complex questions.

Attitudes toward AI are polarized, with hopes and uncertainties.

Sociability and reciprocity are key to community resilience.

Abstract

Online knowledge communities (OKC) such as Stack Exchange, Reddit, and Zhihu have long functioned as socio technical infrastructures for collective problem solving. The rapid adoption of Generative AI (GenAI) introduces both complementarity and substitution. Large language models (LLMs) offer faster, more accessible drafts, yet divert traffic and contributions away from OKC that also provided their training data. To understand how communities adapt under this systemic shock, we report a mixed-methods study combining an online survey (N=217) and interviews with 11 current users. Findings show that while users increasingly rely on AI for convenience, they still turn to OKC for complex, ambiguous, or trust sensitive questions. Participants express polarized attitudes toward AI, reflecting divergent hopes and uncertainties about its role. Yet across perspectives, sustaining sociability, empathy, and reciprocity emerges as essential for community resilience. We argue that GenAI's impact constitutes not a terminal decline but a design challenge: to reimagine socio-technical complementarities that balance automation's efficiency with human judgment, trust, and collective stewardship in the evolving knowledge commons. To decline or sustain, it is now or never to take action.