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Area of Science:

  • Political Science
  • Computer Science
  • Media Studies

Background:

  • Analyzing US presidential campaign television advertisements is crucial for understanding political communication.
  • Previous research has been limited by the manual effort required for data collection and annotation.
  • Existing datasets are often small subsets due to the challenges of manual processing.

Purpose of the Study:

  • To introduce the largest and most comprehensive digital dataset of US presidential campaign television advertisements.
  • To provide machine-searchable transcripts and high-quality summaries for academic research.
  • To demonstrate an automated pipeline for processing video data.

Main Methods:

  • Developed a large-scale, parallelized, AI-based analysis pipeline.
  • Automated video preparation, transcription, storyboarding, and summarization.
  • Applied the pipeline to 9,707 presidential ads from the Julian P. Kanter Political Commercial Archive.
  • Conducted human evaluations to validate transcript and summary quality.

Main Results:

  • Created the largest digital dataset of US presidential campaign ads with automated transcripts and summaries.
  • Human evaluations confirmed the quality of AI-generated transcripts and summaries matches manual alternatives.
  • Demonstrated the dataset's utility through an application tracking issue evolution over seven decades.

Conclusions:

  • The AI-based pipeline successfully automates the creation of high-quality datasets for political advertising research.
  • This comprehensive dataset facilitates in-depth analysis of historical campaign communication strategies.
  • The methodology and codebase offer a scalable solution for summarizing other video datasets using LLM-based tools.