Can the Communication Style of Social Media Videos Affect Listening Quality and Opinion Change?
Bibliographic record
Abstract
Studies of opinion change suggest that disagreements online can contribute to attitude polarization, increasing the extremity of disagreement over political issues. However, very little empirical literature considers the role of listening quality. Can we use videos to motivate higher quality listening and reduce attitude polarization over social media? We fielded an online survey experiment that answers these questions, using a YouTube video containing political messages and allowing survey participants to respond to the video with a comment. We test whether different communication styles – a more inclusive deliberative intervention, personal storytelling and a less inclusive deliberative intervention, a rational-legal style message—impact listening quality and opinion change. We find that YouTube messages about a controversial political issue have a large impact on listening quality, regardless of communication style. We also find that storytelling can have persuasion effects, but only among voters who do not have an opinion on the policy being discussed. Among those who already hold an opinion on the issue, it is difficult to change people’s minds using short-form YouTube videos, regardless of listening quality.
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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 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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".