Exploring Emerging NLP and Machine Learning Methods in Climate Change Discourse Analysis on Social Media: A Systematic Literature Review
Bibliographic record
Abstract
Abstract This study systematically examines emerging methods, particularly NLP and ML, for analyzing climate change discourse on social media platforms. Within this framework, sub-objectives encompass presenting methodological approaches and identifying prevalent climate change themes, and data sources. As climate change communication has evolved rapidly in the digital age, with social media becoming a pivotal arena for public discourse, opinion dissemination, and information exchange. The intersection of ML and NLP techniques offers unprecedented opportunities to transform vast amounts of unstructured data into valuable information, ready to be consumed by climate policymakers and different stakeholders. Drawing upon a comprehensive review of 56 articles, this study identifies and synthesizes six different methods that are further divided into sub-approaches and techniques, addressing climate change themes and platforms used. This research contributes to the literature by presenting the most used and effective methods and identifying potential areas needing more investigation in the future. It also provides insight into trending themes and overlooked ones, offering best practices and future research directions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".