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Generative artificial intelligence and machine learning methods to screen social media content.

Kellen Sharp1, Rachel R Ouellette2, Rujula Singh Rajendra Singh3

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Summary
This summary is machine-generated.

Generative artificial intelligence (AI) and machine learning can screen social media data for research, but human oversight is needed. This study used ChatGPT-4 to analyze TikTok content on pregnancy and vaping, showing potential for initial screening.

Keywords:
ChatGPTComputer visionENDSGenerative AIMachine learningPregnancySocial mediaTikTokVapinge-cigarette

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Area of Science:

  • Computational Social Science
  • Digital Health
  • Artificial Intelligence in Research

Background:

  • Social media research faces challenges with vast, evolving data and irrelevant search results.
  • Effective methods are needed to screen social media content for specific research questions.
  • Generative AI and machine learning offer potential solutions for content screening.

Purpose of the Study:

  • To present generative artificial intelligence (AI) and machine learning methods for screening social media content.
  • To apply these methods to identify TikTok content related to e-cigarette use during pregnancy.
  • To evaluate the effectiveness of ChatGPT-4 in screening this specific content.

Main Methods:

  • Searched TikTok for pregnancy and vaping content using 70 hashtag pairs, obtaining 11,673 posts.
  • Extracted video, description, and metadata; transcribed audio using Whisper; and performed object/text detection.
  • Utilized ChatGPT-4 for content analysis, refined by human coder cross-checking and review.

Main Results:

  • ChatGPT-4 classified 44.86% of videos as pregnancy-related, 36.91% as vaping-related, and 8.91% as both.
  • Human reviewers confirmed vaping and pregnancy content in 45.38% of posts identified by ChatGPT.
  • Human review of excluded posts showed a 99.06% agreement rate with ChatGPT's screening.

Conclusions:

  • ChatGPT demonstrates mixed capacity for screening social media content converted to text via machine learning.
  • ChatGPT's sensitivity was lower than human coders but effective for initial screening and excluding irrelevant content.
  • Future research should focus on enhancing ChatGPT's sensitivity for social media content analysis.