Computer Vision Models for Detecting Large Cigars on Social Media

Clinical Snapshot

Key Findings

• P Value: Not reported

• Effect Size: Recall 0.98, Precision 0.99, F1 score 0.98

• Primary Outcome: Diagnostic accuracy of YOLOv7 model for large cigar detection

• Nnt Or Sensitivity: Sensitivity (recall) 0.98, specificity not reported

• Confidence Interval: Not reported

Clinical Application

Technically feasible but requires significant computational resources and platform-specific adaptation Limited direct relevance to Australian clinical practice; potential application for TGA tobacco product surveillance and research supporting Australian tobacco control policies Public health researchers conducting tobacco surveillance on social media platforms

Abstract

INTRODUCTION: Large cigars, which include premium cigars and large manufactured cigars, are widely promoted on social media and digital media, highlighting the critical need for scalable methods to monitor such content. To fill this need, we aimed to train and test an automated computer vision model to identify large cigars. METHODS: In 2022, we obtained 876 large cigar images and 1526 non-cigar images (ie, cigarettes, e-cigarettes, and pens) from Reddit, a global social media platform organized into subcommunities (subreddits) focused on specific topic areas, discussions, and communities. We used the You Only Look Once Version 7 (YOLOv7) computer vision model to train large cigar detection and evaluated its performance using recall, precision, and F1 scores (ie, harmonic mean of recall and precision scores). The data were split into 75% (594 cigar images and 250 non-cigar images) for training and 25% (282 cigar images) for validating and testing. Non-cigar images (n = 1276) were used to evaluate for false positives. RESULTS: Our model achieved a recall of 0.98, a precision of 0.99, and an F1 score of 0.98, demonstrating high accuracy in correctly identifying large cigars while minimizing false positives. It successfully distinguished large cigars from similarly shaped objects with an accuracy of 97%. CONCLUSIONS: Our computer vision model accurately distinguished large cigars from non-cigar images, demonstrating its potential as an automated, scalable tool for monitoring this understudied tobacco product on social media and other digital media. IMPLICATIONS: This computer vision model offers a novel and scalable approach to tobacco monitoring by automatically detecting large cigars in social media content. Its application may enhance the efficiency of product identification and facilitate research on use patterns, user perceptions, and emerging marketing strategies, thereby generating evidence to inform regulatory decision-making and policies aimed at preventing initiation.