An empirical dataset based on real-world distribution cases, used to predict and interpret matrix effects in multi-platform audiovisual content. The dataset was contributed by Qi Huailiang and last updated on April 13, 2026. It likely contains features related to platform coordination, content structure, and narrative attributes.
Use Cases
- Predict audience attitudes based on platform coordination and content features.
- Model emotional responses to multi-platform content using machine learning.
- Analyze the influence of narrative coherence on reception outcomes.
- Benchmark statistical methods against explainable AI models for media effect prediction.
Strengths
- Dataset is based on real-world distribution cases, providing empirical grounding.
- Study framework combines predictive modeling with SHAP-based explanation methods for interpretability.
- The proposed model reportedly outperforms benchmark statistical methods.
Limitations
- Column-level documentation is absent; field semantics must be inferred after download.
- Row count is unknown, which may limit suitability assessment.
- Description metadata is limited; actual data quality requires manual inspection after download.
Provenance
- Source
- Qi Huailiang Dataverse
- Collection Method
- Likely gathered from empirical cases of real-world multi-platform audiovisual content distribution.
- Time Range
- null
- Freshness
- Last updated 2026-04-13 13:28:18; freshness should be verified.
- Geography
- null