Persuasive Data Storytelling with a Data Video during Covid-19 Infodemic: Affective Pathway to Influence the Users' Perception about Contact Tracing Apps in less than 6 Minutes
Why this work is in the frame
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Bibliographic record
Abstract
The current pandemic showed us the importance of swiftly disseminating data-based information to the masses of people. This study explores an affect-centered narrative to convey data-driven messages regarding contact tracing apps (CTAs) using video as a medium (i.e., data video). A between-subjects online study compared the effect of three storytelling approaches on viewers' perception. A video developed by Google was selected as the baseline video (Control Condition; 2min 23s) due to its high quality and relevance to CTAs. The central messages of this baseline video were; a) how CTAs work, and b) how safe and effective CTAs are. Infographics supporting these messages were then added to the baseline video (the second condition; 3min 19s); this was a simple data video (DV), and it did not intend to induce specific emotional experiences in participants (i.e., cognition-centered video). Finally, an affect-focused DV (AFDV) was also created by emphasizing the emotion-based narrative aspect of the message (the third condition; 4min 6s). In this video, three cute human-like cartoon characters were introduced. Viewers in this condition needed to process both cognitive and affective information. Note all three videos (i.e., control video, DV, and AFDV) conveyed identical messages. Participants watched one of these three videos only once, and we explored the video effect on their perception. Our results repeatedly indicated the potential benefits of including affect in data storytelling.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it