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A Scalable Sampling Approach for Artificial Intelligence-Based Alcohol Content Estimation in Movies.

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Analyzing alcohol depictions in movies is crucial for public health. Sampling at 1 frame per second (fps) significantly reduces processing time with minimal accuracy loss, making large-scale alcohol exposure estimation feasible.

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Area of Science:

  • Computational social science
  • Media studies
  • Public health research

Background:

  • Streaming services increase movie accessibility, leading to greater exposure to alcohol portrayals.
  • Alcohol depictions in media are a known risk factor for increased alcohol consumption.
  • Estimating alcohol exposure in films is challenging due to the extensive time and resources required for frame-by-frame analysis.

Purpose of the Study:

  • To evaluate the impact of reduced frame sampling rates on the accuracy of alcohol exposure estimation in movies.
  • To determine a practical and computationally efficient sampling frequency for large-scale media analysis.

Main Methods:

  • Utilized a LLaVA v1.6 model with 95% accuracy for zero-shot alcohol depiction prediction on 20 feature-length movies.
  • Compared full-framerate (25 fps) analysis against uniform downsampling (1 fps) and sparse interval sampling (1 frame per N seconds).
  • Quantified accuracy loss using a difference score and measured execution time for each sampling method.

Main Results:

  • A sampling frequency of 1 fps achieved an average difference score below 0.10, indicating minimal accuracy loss compared to full-framerate analysis.
  • Reducing sampling to 1 fps resulted in a 25-fold decrease in execution time.
  • Sparser sampling intervals (e.g., 1 frame per 10 seconds) led to significantly higher error scores.

Conclusions:

  • Lowering movie frame sampling frequency to 1 fps offers a practical balance between accuracy and computational efficiency for alcohol exposure studies.
  • 1 fps is a scalable solution for estimating alcohol depictions in large movie datasets.
  • This method facilitates more efficient public health research on media's influence on alcohol use.