VisTopics: A Visual Semantic Unsupervised Approach to Topic Modeling of Video and Image Data
This provides a scalable tool for computational media studies to analyze visual narratives, though it is incremental as it integrates existing methods like Latent Dirichlet Allocation with caption-based analysis.
The study tackled the problem of analyzing large-scale visual datasets to understand media representation by introducing VisTopics, an end-to-end framework that applied semantic clustering to 6,928 deduplicated frames from 452 NBC News videos, uncovering 35 topics such as political events and environmental crises.
Understanding visual narratives is crucial for examining the evolving dynamics of media representation. This study introduces VisTopics, a computational framework designed to analyze large-scale visual datasets through an end-to-end pipeline encompassing frame extraction, deduplication, and semantic clustering. Applying VisTopics to a dataset of 452 NBC News videos resulted in reducing 11,070 frames to 6,928 deduplicated frames, which were then semantically analyzed to uncover 35 topics ranging from political events to environmental crises. By integrating Latent Dirichlet Allocation with caption-based semantic analysis, VisTopics demonstrates its potential to unravel patterns in visual framing across diverse contexts. This approach enables longitudinal studies and cross-platform comparisons, shedding light on the intersection of media, technology, and public discourse. The study validates the method's reliability through human coding accuracy metrics and emphasizes its scalability for communication research. By bridging the gap between visual representation and semantic meaning, VisTopics provides a transformative tool for advancing the methodological toolkit in computational media studies. Future research may leverage VisTopics for comparative analyses across media outlets or geographic regions, offering insights into the shifting landscapes of media narratives and their societal implications.