Emotional framing in online environmental activism: Pairing a Twitter study with an offline experiment
Bibliographic record
Abstract
As the consequences of anthropogenic climate change become more apparent, social media has become a central tool for environmental activists to raise awareness and to mobilize society. In two studies, we examine how the emotional framing of messages posted by environmental activists influences engagement and behavioral intentions toward environmental action. In the first study, tweets ( N = 510k) of 50 environmental activists posted between November 2015 and December 2020 are examined to measure their emotional content and its relation to tweet diffusion. Environment-related tweets are found to be shared more the less they contain positive emotion and the more they contain negative emotion. This result supports the negativity bias on social media. In Study 2 ( N = 200), we experimentally test whether negatively vs. positively framed environmental content leads to increased reported intent to engage with collective action, and whether mood mediates that link. We find both direct and indirect effects on reported climate action intentions when mood is used as a mediator. The negative mood resulting from seeing negative tweets makes participants more likely to report higher action intention (indirect effect)—congruent with Study 1. However, seeing negative tweets also makes participants less inclined to act (direct effect), indicating a suppression effect and the presence of other factors at work on the pathway between information and action intent formation. This work highlights the complex and multifaceted nature of this relation and motivates more experimental work to identify other relevant factors, as well as how they relate to one another.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".