Paid Voices vs. Public Feeds: Interpretable Cross-Platform Theme-Based Analysis of Climate Discourse
Mirrored from arXiv — NLP / Computation & Language for archival readability. Support the source by reading on the original site.
Computer Science > Computation and Language
Title:Paid Voices vs. Public Feeds: Interpretable Cross-Platform Theme-Based Analysis of Climate Discourse
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)
Access Paper:
- View PDF
- HTML (experimental)
- TeX Source
Current browse context:
References & Citations
Bibliographic and Citation Tools
Code, Data and Media Associated with this Article
Demos
Recommenders and Search Tools
arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
More from arXiv — NLP / Computation & Language
-
Generating in the Limit with Infinitely Many Hallucinations
Jun 30
-
Extracting Knowledge from an Arabic-English Machine-Readable Dictionary Using Information Extraction
Jun 30
-
Developmental Trajectories of Situation Modeling and Mentalizing in Transformer Language Models
Jun 30
-
A French OSCE Dialogue Dataset and Controllable Virtual Patient System for Clinical Training
Jun 30
Discussion (0)
Sign in to join the discussion. Free account, 30 seconds — email code or GitHub.
Sign in →No comments yet. Sign in and be the first to say something.