  The U.S. Food & Drug Administration (FDA) requires that e-cigarette
advertisements include a prominent warning label that reminds consumers that
nicotine is addictive. However, the high volume of vaping-related posts on
social media makes compliance auditing expensive and time-consuming, suggesting
that an automated, scalable method is needed. We sought to develop and evaluate
a deep learning system designed to automatically determine if an Instagram post
promotes vaping, and if so, if an FDA-compliant warning label was included or
if a non-compliant warning label was visible in the image. We compiled and
labeled a dataset of 4,363 Instagram images, of which 44% were vaping-related,
3% contained FDA-compliant warning labels, and 4% contained non-compliant
labels. Using a 20% test set for evaluation, we tested multiple neural network
variations: image processing backbone model (Inceptionv3, ResNet50,
EfficientNet), data augmentation, progressive layer unfreezing, output bias
initialization designed for class imbalance, and multitask learning. Our final
model achieved an area under the curve (AUC) and [accuracy] of 0.97 [92%] on
vaping classification, 0.99 [99%] on FDA-compliant warning labels, and 0.94
[97%] on non-compliant warning labels. We conclude that deep learning models
can effectively identify vaping posts on Instagram and track compliance with
FDA warning label requirements.
