Computational approaches for democratizing online community governance
type: CreativeWork
description: Although democracy is a common real-world governance model, large swaths of the social media landscape are run in a quasi-dictatorial manner. From Usenet newsgroups to Reddit subreddits, online communities are managed by volunteer moderators who have sole authority to create and enforce community rules. While rules are vital to community health (preventing trolling, keeping communities on-topic, etc), community members have little voice in their creation or implementation. Part of this is due to inadequate tooling. Online communities require democratic governance technologies that are able to function within the unique dynamics of online social life: massive scale, highly fluid user populations, and pseudo-anonymous communication. Although the potential benefits of democratizing online community governance have been broadly argued for in prior work, designing effective governance technologies has remained elusive. As such, this thesis has dual goals. First, I seek to conduct quantitative analysis to better understand the contexts in which online communities should utilize democratization. Second, I want to use this analysis to inform the design and deployment of new democratization technologies. Concretely, I provide evidence that democratization efforts may have the most impact if they target what I call the “practice”-level of governance, focusing on the application of rules to specific cases. Further, I highlight the potential problems that user-base fluidity poses to democratic governance: when a community doubles in size overnight, whose concerns should prioritized? Those of long time users? Or newcomers? In light of these insights, I design Venire, a machine-learning based tool for facilitating practice-level democratization. My work provides a roadmap for future efforts to democratize online community governance, ensuring that the online social spaces we participate in actually reflect our values.
abstract: Although democracy is a common real-world governance model, large swaths of the social media landscape are run in a quasi-dictatorial manner. From Usenet newsgroups to Reddit subreddits, online communities are managed by volunteer moderators who have sole authority to create and enforce community rules. While rules are vital to community health (preventing trolling, keeping communities on-topic, etc), community members have little voice in their creation or implementation. Part of this is due to inadequate tooling. Online communities require democratic governance technologies that are able to function within the unique dynamics of online social life: massive scale, highly fluid user populations, and pseudo-anonymous communication. Although the potential benefits of democratizing online community governance have been broadly argued for in prior work, designing effective governance technologies has remained elusive. As such, this thesis has dual goals. First, I seek to conduct quantitative analysis to better understand the contexts in which online communities should utilize democratization. Second, I want to use this analysis to inform the design and deployment of new democratization technologies. Concretely, I provide evidence that democratization efforts may have the most impact if they target what I call the “practice”-level of governance, focusing on the application of rules to specific cases. Further, I highlight the potential problems that user-base fluidity poses to democratic governance: when a community doubles in size overnight, whose concerns should prioritized? Those of long time users? Or newcomers? In light of these insights, I design Venire, a machine-learning based tool for facilitating practice-level democratization. My work provides a roadmap for future efforts to democratize online community governance, ensuring that the online social spaces we participate in actually reflect our values.
author: Koshy, Vinay
language: en
keywords: Human-Computer Interaction, Human-AI interaction, Computational Social Science, Computer Supported and Cooperative Work, Social Computing, Online Communities
date published: 2025-12
date modified: 2026-07-22T22:25:07Z
provider: University of Illinois - Urbana-Champaign
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: University of Illinois Urbana-Champaign

PropertyValue
property ID: Handle
value: https://hdl.handle.net/2142/132457