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Characterizing Anti-Vaping Posts for Effective Communication on Instagram Using Multimodal Deep Learning.

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Area of Science:

  • Social Media Research
  • Public Health Communication
  • Artificial Intelligence in Health

Background:

  • Instagram is a popular platform for youth and young adults.
  • Understanding social media engagement with anti-vaping content is crucial.
  • Current anti-vaping messages often lack user engagement.

Purpose of the Study:

  • Identify key features in anti-vaping Instagram posts associated with high user engagement.
  • Utilize artificial intelligence (AI) to analyze a large dataset of posts.
  • Inform effective public health communication strategies regarding vaping.

Main Methods:

  • Collected 8972 anti-vaping Instagram image posts.
  • Hand-coded 2200 images to identify nine features.
  • Employed deep learning (OpenAI's contrastive language-image pre-training with ViT-B/32) and logistic regression for feature extraction and classification.
  • Used Latent Dirichlet Allocation and Valence Aware Dictionary and sEntiment Reasoner for topic and sentiment analysis.
  • Applied negative binomial regression to assess feature association with engagement metrics (likes, comments).

Main Results:

  • Features like educational warnings and warning signs were significantly associated with higher engagement.
  • Posts discussing health risks of vaping received more engagement than those on quitting.
  • AI-labeled posts (8972) identified more significant features than hand-coded posts (2200).

Conclusions:

  • Key features in anti-vaping Instagram posts can enhance communication about e-cigarette health effects.
  • AI-driven analysis of social media content offers effective strategies for public health messaging.
  • Findings can help combat the youth vaping epidemic by improving anti-vaping campaign reach.