Mutual influences between climate change communication and expressive participation on Weibo: A longitudinal network–behaviour co-evolution analysis
Bibliographic record
Abstract
Drawing on Reinforcing Spirals Model theory and longitudinal network analysis methods, we analyse the co-evolution of climate change communication network and online expressive participation around climate policy issues on China’s top social media platform Weibo (2019–2021). We find a mutual influence between actors’ engagement in communication relationships and their participatory behaviours over time. However, this dynamic does not operate at the individual level. Active/popular actors in the communication network do not necessarily become more participatory later, nor does higher participation significantly lead to greater activity/popularity in the subsequent time point. Instead, this dynamic is characterized by concurrent homogeneity-based network selection and network influence processes. Model results show that actors with similar participation levels are more likely to form future communication ties and actors with existing communication ties are more likely to converge in participation levels over time. This indicates a homogeneity-based reciprocal influence between network connections and individual engagement. We also find that endogenous network structural factors play a significant role in shaping people’s engagement with climate change communication. These findings underscore the importance of the social relational dimension in the dynamic interplay between communication and online expressive participation, contributing to a more nuanced understanding of the Reinforcing Spirals Model theory.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".