[Submitted on 11 Aug 2026]
Title:AI-inferred expressed well-being and collective-action discourse in climate-change campaigns on X
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Abstract:Climate campaigns are often evaluated through attention and mobilization, but less is known about the well-being language that accompanies them. Whether campaign periods alter positive affect and hope, and whether happiness aligns with action language, remains unresolved. We analysed 364,118 public Twitter/X posts from Earth Day, Earth Hour, Global Climate Action Day and World Environment Day in 19 occurrence-years, using 30-day pre-event, event and post-event windows. A versioned weighted lexical model estimated happiness, future-oriented hope, collective capability, distress and action language. Event-period happiness prevalence was 9.02 percentage points higher than the pre-event baseline , whereas paired occurrence contrasts showed a 10.75-point decline in action language, indicating a happiness--action divergence. The happiness estimate remained positive across composition and text-deduplication checks, but was less precise under a 19-cluster wild bootstrap. Happier source posts had lower odds of an observed matched retweet cascade.
Subjects:
Computation and Language (cs.CL); Computers and Society (cs.CY)
Cite as: arXiv:2609.22096 [cs.CL]
(or arXiv:2609.22096v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2609.22096
arXiv-issued DOI via DataCite
Submission history
From: Wentao Xu [view email] [v1] Tue, 11 Aug 2026 15:11:19 UTC (524 KB)
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