Mapping Climate Change Communication: A Social Network and Discourse Analysis Approach
Bibliographic record
Abstract
Climate change communication increasingly unfolds within digital environments, where social media platforms such as Twitter play a pivotal role in shaping public discourse (Papacharissi, 2015). This study combines Social Network Analysis (SNA) and Discourse Analysis (DA) to explore how climate change is discussed and structured through Twitter interactions. A dataset of tweets mentioning Greta Thunberg and Donald Trump—the two most frequently mentioned figures identified through network centrality measures—was analyzed. Using Gephi software, network properties such as modularity, degree centrality, and graph diameter were evaluated and visualized through the Force Atlas layout. The findings reveal a fragmented but interconnected network structure, with modular clusters aligned with political, activist, and organizational affiliations. Discourse analysis of the tweets highlights contrasting narrative strategies: while Greta Thunberg is framed through language of solidarity, urgency, and mobilization, Donald Trump is predominantly referenced through oppositional and critical discourse. These patterns exemplify the dynamics of connective action in digital environments, where personal engagement drives collective narratives (Bennett & Segerberg, 2012). Overall, the results suggest that Twitter reflects and amplifies emotional and ideological currents within the climate change debate, fostering both polarization and solidarity across different stakeholder communities. This study contributes to understanding the complex interplay between digital communication structures and climate change narratives.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.015 | 0.009 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".