Koyama, Yoko, Chen, Gongfan, Saxena, Mansi · Nov 6, 2026
The Infrastructure Divide: Analyzing Community Stance on Data Center Development via Reddit
Markdown · JSON · Original source
Authors Yoko Koyama, Gongfan Chen, and Mansi Saxena present an -powered framework for analyzing Reddit discussion of data center development. Applied to 798 posts and 102,328 associated comments spanning a decade, it classified stance at 83% of the human inter-reviewer agreement ceiling. The authors report that against posts outnumbered favor posts 2.6:1 and drew 8.5 times more comments, with the gap widening over 86 weeks. They identify four clusters of opposition arguments: resource consumption and scarcity; siting processes and secrecy; political opposition; and political and corporate accountability. Posts with emotional or provocative political framing attracted the most engagement, while technical concerns such as water and power appeared across lower-engagement comment threads. The authors argue that these findings could give local leaders, developers, and policy makers a quantified, continuously updatable basis for prioritizing concerns and policy levers, supporting a shift toward negotiated, verifiable partnership. The study measures Reddit discourse; the abstract does not establish that its balance of views represents affected communities.
Headline and summary by Escape Meridian, prepared by Luna from the attributed source. A source check is not independent confirmation of the source’s claims.
Original source
cs.AI · Article
Source details
Original source date: Nov 6, 2026 (2026-11-06).
Date basis: Date basis not provided.
Added to Escape Meridian: 2026-11-12T06:17:51.515Z. First observed: 2026-11-10T08:42:27.047Z. Retrieved: 2026-11-12T06:16:59.677Z.
Referenced by
Recorded citations and public file attachments. A reference alone does not establish agreement or influence.
No public references recorded here yet.
Limits
The analysis is based on Reddit posts and comments; the abstract does not establish that the observed stance patterns represent the views of communities affected by data center development. The abstract describes the framework and headline findings but does not provide enough methodological detail to assess the classification or topic-cluster results beyond the reported comparison with human inter-reviewer agreement.
Open question
How closely do the stance patterns in this Reddit sample reflect the views of communities affected by data center development?
Source-stated
The authors present an LLM-powered framework and apply it to Reddit posts and comments about data center development.
this study presents an LLM-powered opinion-mining framework and applies it to 798 Reddit posts and 102,328 associated comments spanning a decade.
Source-stated
The framework's stance classification reached 83% of the human inter-reviewer agreement ceiling.
classified stance at 83% of the human inter-reviewer agreement ceiling.
Source-stated
The authors report more against than favor posts and substantially more comments on against posts.
against posts outnumbered favor posts 2.6:1 and generated 8.5 times more comments
Source-stated
The authors group opposition arguments into four topic clusters, led by resource consumption and scarcity, followed by siting processes and secrecy.
resource consumption and scarcity (42.8%), contested siting processes and secrecy (34.2%)
Source-stated
The authors report that emotionally or politically provocative framing drew the most engagement, while technical concerns appeared in lower-engagement threads.
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.
Source-stated
The authors propose the findings as a basis for prioritizing concerns and policy levers, with the stated aim of supporting negotiated partnership.
a quantified, continuously updatable basis for prioritizing concerns and policy levers, supporting a shift from opposition to negotiated, verifiable partnership
Source and contributor context
Koyama, Yoko, Chen, Gongfan, Saxena, Mansi · Article author
These are credits stated by the source; they do not establish a profile match or authorship of every passage.
Source media
No suitable media was found by the supported page extraction.
Terms and references
Reference context selected by Escape Meridian; separate from source evidence. Entity mentions do not establish authorship, involvement or endorsement.
Large language model
A language model with many learned numerical settings, used to process and generate language.
arXiv
arXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed.
Editorial provenance
Version 1. Publication ID: 14ab628d-4e70-4ccd-84dd-1b0714a961b0.
Prepared by gpt-6-luna; grounding review by gpt-6-luna.
Source check: uncertain by anthropic/claude-opus-5.5 on 2026-11-12T06:17:51.515Z. This checks fidelity to the source, not whether its claims are independently true.
Editorial selection: accept; reasons: material_development.
Observation: ea95ef690703f06e5ae22ed0a5971bd1e4937e84dea209374cb314534ef72257. Source text hash: 525df5aae41d8267bafa500c9344b2009b5758129669a13afe166e1cad961b10.
Revision and source-check history
Version 1 · 2026-11-12T06:17:51.515Z
The Infrastructure Divide: Analyzing Community Stance on Data Center Development via Reddit
Authors Yoko Koyama, Gongfan Chen, and Mansi Saxena present an -powered framework for analyzing Reddit discussion of data center development. Applied to 798 posts and 102,328 associated comments spanning a decade, it classified stance at 83% of the human inter-reviewer agreement ceiling. The authors report that against posts outnumbered favor posts 2.6:1 and drew 8.5 times more comments, with the gap widening over 86 weeks. They identify four clusters of opposition arguments: resource consumption and scarcity; siting processes and secrecy; political opposition; and political and corporate accountability. Posts with emotional or provocative political framing attracted the most engagement, while technical concerns such as water and power appeared across lower-engagement comment threads. The authors argue that these findings could give local leaders, developers, and policy makers a quantified, continuously updatable basis for prioritizing concerns and policy levers, supporting a shift toward negotiated, verifiable partnership. The study measures Reddit discourse; the abstract does not establish that its balance of views represents affected communities.
Original source
cs.AI · Article
Source details
Original source date: Nov 6, 2026 (2026-11-06).
Date basis: Date basis not provided.
Added to Escape Meridian: 2026-11-12T06:17:51.515Z. First observed: 2026-11-10T08:42:27.047Z. Retrieved: 2026-11-12T06:16:59.677Z.
Limits
The analysis is based on Reddit posts and comments; the abstract does not establish that the observed stance patterns represent the views of communities affected by data center development. The abstract describes the framework and headline findings but does not provide enough methodological detail to assess the classification or topic-cluster results beyond the reported comparison with human inter-reviewer agreement.
Open question
How closely do the stance patterns in this Reddit sample reflect the views of communities affected by data center development?
Source-stated
The authors present an LLM-powered framework and apply it to Reddit posts and comments about data center development.
this study presents an LLM-powered opinion-mining framework and applies it to 798 Reddit posts and 102,328 associated comments spanning a decade.
Source-stated
The framework's stance classification reached 83% of the human inter-reviewer agreement ceiling.
classified stance at 83% of the human inter-reviewer agreement ceiling.
Source-stated
The authors report more against than favor posts and substantially more comments on against posts.
against posts outnumbered favor posts 2.6:1 and generated 8.5 times more comments
Source-stated
The authors group opposition arguments into four topic clusters, led by resource consumption and scarcity, followed by siting processes and secrecy.
resource consumption and scarcity (42.8%), contested siting processes and secrecy (34.2%)
Source-stated
The authors report that emotionally or politically provocative framing drew the most engagement, while technical concerns appeared in lower-engagement threads.
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.
Source-stated
The authors propose the findings as a basis for prioritizing concerns and policy levers, with the stated aim of supporting negotiated partnership.
a quantified, continuously updatable basis for prioritizing concerns and policy levers, supporting a shift from opposition to negotiated, verifiable partnership
Source and contributor context
Koyama, Yoko, Chen, Gongfan, Saxena, Mansi · Article author
These are credits stated by the source; they do not establish a profile match or authorship of every passage.
Source media
No suitable media was found by the supported page extraction.
Terms and references
Reference context selected by Escape Meridian; separate from source evidence. Entity mentions do not establish authorship, involvement or endorsement.
Large language model
A language model with many learned numerical settings, used to process and generate language.
arXiv
arXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed.
Editorial provenance
Version 1. Publication ID: 14ab628d-4e70-4ccd-84dd-1b0714a961b0.
Prepared by gpt-6-luna; grounding review by gpt-6-luna.
Source check: uncertain by anthropic/claude-opus-5.5 on 2026-11-12T06:17:51.515Z. This checks fidelity to the source, not whether its claims are independently true.
Editorial selection: accept; reasons: material_development.
Observation: ea95ef690703f06e5ae22ed0a5971bd1e4937e84dea209374cb314534ef72257. Source text hash: 525df5aae41d8267bafa500c9344b2009b5758129669a13afe166e1cad961b10.
uncertain · 2026-11-12T06:17:51.515Z · anthropic/claude-opus-5.5
Publication: 14ab628d-4e70-4ccd-84dd-1b0714a961b0
attribution — pass: The draft correctly attributes the paper to Yoko Koyama, Gongfan Chen and Mansi Saxena. Findings and recommendations are framed as what the authors report or argue, not as established fact.
Authors:Yoko Koyama, Gongfan Chen, Mansi Saxena
summary — uncertain: The summary is faithful but leaves out context a general reader needs. It never says the construction is AI-driven, and it omits the abstract's motivation that an estimated $64 billion in U.S. projects were blocked or delayed. It also drops the authors' point that opposition dominated discourse volume rather than disapproval. 'Stance' and '83% of the human inter-reviewer agreement ceiling' are unexplained. Smallest repair: add one sentence on AI-driven expansion and the $64B figure, add the volume-versus-disapproval caveat, and briefly explain the agreement-ceiling metric, ideally with a term-library tooltip.
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
date — pass: originalPublished is 2026-11-06, which matches the v1 submission date. The feed date of 2026-11-10 is correctly not used.
[Submitted on 6 Nov 2026]
support — pass: Every claim matches the abstract's figures and wording. The policy usefulness is presented as the authors' argument. The limitation and the question about whether Reddit represents affected communities are appropriately conditional and do not overreach.
against posts outnumbered favor posts 2.6:1 and generated 8.5 times more comments, and this gap widened significantly over 86 weeks
media — pass: The selected media is the verified direct arXiv abstract page for this paper, which is the most direct access to the source.
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A language model with many learned numerical settings, used to process and generate language.
Definition referencearXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed.
Explore arXivA language model with many learned numerical settings, used to process and generate language.
Definition referencearXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed.
Explore arXiv