arXiv:2611.10109v1 Announce Type: new
Abstract: The rapid expansion of AI-driven data center construction has outpaced public engagement: an estimated $64 billion in U.S. data center projects were blocked or delayed by local opposition between May 2024 and March 2025, yet the concerns driving this resistance remain poorly characterized. Prior engagement methods such as surveys and hearings are introduced late in the planning process, and no study has systematically mined social media to measure community stance at scale. To address this gap, this study presents an LLM-powered opinion-mining framework and applies it to 798 Reddit posts and 102,328 associated comments spanning a decade. An open-source large language model, guided by a Chain-of-Stance prompting framework and three domain-specific labeling rules, classified stance at 83% of the human inter-reviewer agreement ceiling. Opposition dominated discourse volume rather than disapproval: against posts outnumbered favor posts 2.6:1 and generated 8.5 times more comments, and this gap widened significantly over 86 weeks. Topic modeling of opposition arguments resolved into four reproducible clusters, resource consumption and scarcity (42.8%), contested siting processes and secrecy (34.2%), political opposition to data centers (13%), and political and corporate accountability (8%). Engagement analysis revealed that posts with emotional or provocative political framing attracted the most engagement, while key technical concerns (e.g., water, power) were dispersed across lower-engagement comment threads. These findings give local leaders, developers, and policy makers a quantified, continuously updatable basis for prioritizing concerns and policy levers, supporting a shift from opposition to negotiated, verifiable partnership for data center development.
Abstract: The rapid expansion of AI-driven data center construction has outpaced public engagement: an estimated $64 billion in U.S. data center projects were blocked or delayed by local opposition between May 2024 and March 2025, yet the concerns driving this resistance remain poorly characterized. Prior engagement methods such as surveys and hearings are introduced late in the planning process, and no study has systematically mined social media to measure community stance at scale. To address this gap, this study presents an LLM-powered opinion-mining framework and applies it to 798 Reddit posts and 102,328 associated comments spanning a decade. An open-source large language model, guided by a Chain-of-Stance prompting framework and three domain-specific labeling rules, classified stance at 83% of the human inter-reviewer agreement ceiling. Opposition dominated discourse volume rather than disapproval: against posts outnumbered favor posts 2.6:1 and generated 8.5 times more comments, and this gap widened significantly over 86 weeks. Topic modeling of opposition arguments resolved into four reproducible clusters, resource consumption and scarcity (42.8%), contested siting processes and secrecy (34.2%), political opposition to data centers (13%), and political and corporate accountability (8%). Engagement analysis revealed that posts with emotional or provocative political framing attracted the most engagement, while key technical concerns (e.g., water, power) were dispersed across lower-engagement comment threads. These findings give local leaders, developers, and policy makers a quantified, continuously updatable basis for prioritizing concerns and policy levers, supporting a shift from opposition to negotiated, verifiable partnership for data center development.
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The Infrastructure Divide: Analyzing Community Stance on Data Center Development via Reddit