arXiv — NLP / Computation & Language · · 3 min read

Paid Voices vs. Public Feeds: Interpretable Cross-Platform Theme-Based Analysis of Climate Discourse

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Computer Science > Computation and Language

arXiv:2601.13317 (cs)
[Submitted on 19 Jan 2026 (v1), last revised 24 Jun 2026 (this version, v2)]

Title:Paid Voices vs. Public Feeds: Interpretable Cross-Platform Theme-Based Analysis of Climate Discourse

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Abstract:Climate discourse online shapes public understanding of climate change and informs political and policy debate, yet it unfolds across structurally different environments: paid advertising platforms host targeted, institutionally produced messaging, while public social media reflects largely organic, user-driven discussion. We present a comparative analysis of climate discourse across paid advertisements on Meta (previously Facebook) and public posts on Bluesky from July 2024 to September 2025. To support it, we develop an interpretable thematic discovery pipeline that clusters texts by semantic similarity and uses large language models (LLMs) to label clusters with concise, human-interpretable themes, requiring no predefined topic inventory or seed set. Using these themes, we find the two environments diverge systematically: paid advertising centers on strategic promotion of specific solutions in a formal, forward-looking register, whereas organic discourse centers on systemic critique in a crisis-oriented, scientifically grounded one. We also evaluate the utility of the discovered themes through downstream stance prediction and theme-guided retrieval tasks. While our analysis focuses on climate communication, the framework generalizes to comparative thematic analysis across heterogeneous communication environments.
Subjects: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Machine Learning (cs.LG); Social and Information Networks (cs.SI)
Cite as: arXiv:2601.13317 [cs.CL]
  (or arXiv:2601.13317v2 [cs.CL] for this version)
  https://doi.org/10.48550/arXiv.2601.13317
arXiv-issued DOI via DataCite

Submission history

From: Tunazzina Islam [view email]
[v1] Mon, 19 Jan 2026 19:00:56 UTC (19,686 KB)
[v2] Wed, 24 Jun 2026 16:54:41 UTC (37,410 KB)
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