Skip to content

Video classification and content analysis

Methods for automatically categorizing video content and extracting semantic information from video, including approaches that leverage captions, metadata, frames, and multimodal signals.

Approaches

Caption-based classification:
Using transcripts or auto-generated captions as text features for video categorization; effective for low-cost analysis of large video corpora but depends on caption quality and availability.

Multimodal analysis:
Combining visual (frames, faces, objects), audio, and textual signals to understand video content holistically; can capture nuances missed by single modalities.

Metadata-based methods:
Leveraging video metadata (title, description, view count, comments) as classification features; simple but often insufficient for fine-grained categorization.

Frame-level analysis:
Analyzing individual video frames or keyframes via computer vision (object detection, scene recognition, deepfake detection) to assess visual content.

Key papers in this wiki

  • Misinformation Detection on YouTube Using Video Captions — Demonstrates that pre-trained word embeddings applied to YouTube video captions effectively classify videos as misinformation, debunking, or neutral; achieves 0.92–0.95 binary F1-score; shows metadata-only approaches insufficient.