Combining Data Visualization and Interactive Narrative: A Persuasive Approach to Raise Climate Change Awareness
Bibliographic record
Abstract
Climate change is a global phenomenon that affects every living being on our planet. Raising awareness among people about climate change and helping them realize the possible consequences of their actions is key to mitigating climate change problems. Our research was aimed at achieving this by building a persuasive intervention that combines visualization of climate change data and an interactive narrative that demonstrates how our actions can impact the climate. We conducted a user study with 100 participants and found evidence showing that our system was effective in significantly promoting behavioral intention to mitigate climate change. We found defensive responses as a key factor that is negatively influencing the effect of our intervention on the participants. Compelling visuals and multiple interaction options, simulating climate actions and their consequences, and reducing the effort to learn about the phenomenon were significant positive techniques used in the intervention. Additionally, the social elements of our intervention played a major role in promoting participants’ willingness to perform proenvironmental behavior. Our work contributes to the field of persuasive technology, data visualization, interactive narratives, and climate research by introducing a new persuasive way of communicating climate change information to the public using a combination of data visualizations and interactive 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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".