Computer Vision Models for Detecting Large Cigars on Social Media

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.

Introduction

Cigars include premium cigars, large manufactured cigars (LMCs), cigarillos, and filtered little cigars.¹ Although global data on cigar use are limited, past-30-day prevalence rates among US adults in 2021 were 0.9%, 0.4%, 1.1%, and 0.6%, respectively.² In the United States, cigars as a group are the most used combustible tobacco product by youth³ and adults⁴ after cigarettes. “Large cigars” include premium cigars and LMCs, which are both larger and heavier than cigarillos and filtered little cigars; but they differ in production and marketing: premium cigars are hand-rolled, contain no characterizing flavors, and are marketed as higher-quality products with a higher price point, whereas LMCs are machine-made, often flavored, and typically sold at a lower cost.¹ Regardless of cigar type, cigar smoking is not safe; cigars contain high levels of carcinogenic compounds and other harmful constituents, increasing the risk of oral, esophageal, laryngeal, and lung cancers.¹

Tobacco product promotion, including cigar promotion, on social media has been rampant in recent years.^5-8 The tobacco industry uses social media to enhance product visibility, circumvent tobacco marketing restrictions on traditional media, and reach many people quickly at a low cost. However, despite the tobacco industry’s extensive presence on social media, there is limited knowledge about the marketing of large cigars on these platforms. Much of the existing examination has focused on cigarillos and little cigars.^7,9,10 However, evidence shows that premium cigar brands are using their own social media accounts to promote their products using a variety of themes, including content promoting cigar co-use with alcohol, social events, culture, and celebrity endorsement.⁶

Surveillance of premium cigars’ activities on social media is necessary; however, it is essential to broaden the scope to include large cigars and LMCs, as these products are visually indistinguishable. Currently, there are many advanced computational tools to facilitate surveillance of social media content. For instance, advanced computer vision models, which use deep machine learning, can identify various types of objects within an image, including e-cigarette use on social media.^11,12 Object detection uses features of images (eg, color, brightness, shapes, and contours) to identify and categorize them into prespecified categories.¹³ Recently, this method has been successfully used to identify images of e-cigarettes, exhaled aerosol from e-cigarettes, and people using e-cigarettes on social media with high precision.^11,12 However, this method has not yet been explored for automatically identifying large cigars. This study aims to develop and evaluate a computer vision model to detect large cigars on social media. If successful, the computer vision model can provide automated, scalable surveillance of large cigars on social media and other digital platforms, enhancing the efficiency of tobacco content monitoring—especially for large cigars, which are often overlooked.

Methods

Data Collection

To train our model, we collected images of large cigars from Reddit between September and December 2022. Reddit is a social media platform composed of subcommunities (ie, subreddits) focused on a specific topic area. Members of subreddits can post text, image, or video-based content. Reddit users typically post anonymously and are usually globally distributed, so it is generally impossible to pinpoint their geographical location. We chose the subreddits r/cigars and r/EveryDayIsCigarDay, which are focused on large cigars, to obtain images. We selected these subreddits because they provided centralized access to a wide array of large cigar images, provided by subreddit users. These subreddits focused on cigars and had particularly high numbers of members (~168 000 members) and levels of engagement (~50 posts per day). Using the Reddit developer API, we scraped 5000 most recent posts on these two cigar subreddits, extracting all available images. We also scraped images from 10 000 posts from three other subreddits (ie, r/Cigarettes, r/VapePorn, and r/pen) and extracted non-cigar images to train the model to distinguish between cigars and other tobacco products (ie, cigarettes, e-cigarettes) and other similar objects (ie, pens). We removed duplicates and irrelevant images (eg, non-cigar-related content within the cigar dataset and non-e-cigarette or cigarette or non-pen images from the non-cigar dataset), and low-resolution images. Including high-resolution images with sufficient visual clarity in the initial training data is important so that the model can first learn robust and generalizable features of large cigars from images and including low-resolution images may degrade model performance. In total, we obtained 876 cigar images and 1526 non-cigar images (ie, cigarettes, e-cigarettes, and pens).

Preprocessing and Annotation

We normalized the raw images by resizing them to a lower resolution at 200 × 200 to ensure uniformity and reduce model training time, as larger images increase computational demand. A member of the research team annotated cigar images by drawing bounding boxes (ie, a rectangular box) around a cigar in each image using the open-source image annotation tool, labelImg. Drawing bounding boxes around the images is only needed during the training phase of the model, when the model is learning to identify cigars. Once the training is complete, the model can identify cigars and automatically add bounding boxes on newly classified images.

Object Detection Model Training

We deployed a You Only Look Once Version 7 (YOLO v7)¹⁴—computer object detection model—to identify large cigars automatically. This model uses convolutional neural machine learning methods, a deep learning method specifically developed for structured data like images. Object detection in YOLO v7 uses the training data, where objects of interest (large cigars) are framed within bounding boxes, to predict their presence in the validation dataset. Next, we evaluated the model’s performance using separate testing datasets—one with annotated images of cigars and another with annotated images of non-cigars. Model performance is measured by the model’s accuracy in correctly identifying large cigars and producing low false positives (ie, incorrectly identifying the presence of a large cigar in the non-cigar dataset).

We split our dataset into 75% (594 cigar images and 250 non-cigar images) for training and 25% for validating and testing (ie, 12% [136 cigar images] for validating and 13% [146 cigar images] for testing). A test dataset of non-cigar images (n = 1276) was used to assess for false positives. See Figure 1 for the workflow chart.

Figure 1.

Workflow diagram illustrating image collection, processing, and dataset division. Workflow diagram illustrating image collection, processing, and dataset division. Posts were collected from Reddit communities: r/EveryDayIsCigarDay and r/cigars (5000 posts), and r/pen, r/VapePorn, and r/cigarettes (10 000 posts). After extracting and preprocessing images, 876 were annotated as cigar images and 1526 as non-cigar images. Cigar and non-cigar images were then divided into datasets: a training set (594 cigar and 250 non-cigar images), a validating set (136 cigar images), and two testing sets (a: 146 cigar images, b: 1276 non-cigar images).

The model performance was evaluated for accuracy (the percentage of correct predictions cross the dataset), precision (the percentage of correct positive predictions), recall (the proportion of actual positives correctly identified), and F1 (the harmonic average of precision and recall scores). These metrics range from 0 (lowest) to 1 (highest). The confidence score (ranging from 0 [lowest] to −1 [highest]) indicates the model’s certainty in identifying an object within the bounding box.

Results

Our object detection model’s performance using the test dataset of cigar images was: 0.98 recall, 0.99 precision, and 0.98 F1 score. In addition, the test dataset of non-cigar images demonstrated that our model successfully differentiated large cigars from similarly shaped objects (ie, cigarettes, e-cigarettes, and pens) with 97% accuracy. See Figure 2 for examples of large cigar identification.

Figure 2.

Example of large cigar detection with bounding boxes and confidence values. Four panels showing examples of automated cigar image detection with bounding boxes and confidence scores. First panel, “Cigar and cigarette”: a cigar and a cigarette are detected, with the cigar boxed and labeled with confidence 0.82. Second panel, “Cigar among cigarettes”: one cigar is boxed among several cigarettes, labeled with confidence 0.92. Third panel, “Cigar on a glass”: a cigar resting on a drinking glass is boxed, labeled with confidence 0.78. Fourth panel, “multiple cigars”: three cigars are held by different hands, each boxed with confidence scores of 0.82, 0.92, and 0.68.

Discussion

We developed an automated, scalable computer vision model that successfully detected large cigars and distinguished them from other tobacco products (ie, cigarettes and e-cigarettes) and similarly shaped objects such as pens in images obtained on social media. Prior studies have used similar computer vision models to identify other nicotine and tobacco products, such as e-cigarettes, by detecting a large cloud of vapor, hands holding or using the devices, and warning labels,^11,12 and hookah products¹⁵ from social media posts. Our study is the first to apply computer vision models to identify large cigars, contributing to the growing body of literature on this topic and offering a tool that could facilitate surveillance of large cigar content on social media and other digital media by automatically detecting their presence in images.

Surveillance of social media and other digital media remains critical as promotional activities on these platforms remain largely unchecked and are not currently fully covered by the advertising restrictions applied to cigarettes and smokeless tobacco under the Master Settlement Agreement in the United States.¹⁶ Moreover, premium cigars are no longer under the regulatory authority of the US FDA because of the federal court’s decision to vacate the FDA’s authority to regulate premium cigars.¹⁷ In light of the current lack of regulatory authority over premium cigars, surveillance of large cigar marketing, including premium cigars and LMCs, on social media is essential. Left unchecked, premium cigar brands and companies can continue to promote their products on social media, increasing exposure among youth and young adults. Premium cigars and LMCs are indistinguishable from a visual perspective. Thus, it is possible that premium cigar marketing may influence LMC use and vice versa, highlighting the need for surveillance to include both premium cigars and LMC. Object detection methods such as the one developed and tested in this article can facilitate surveillance of all large cigars across social media platforms.

Aside from the US regulatory framework, the World Health Organization (WHO) Framework Convention of Tobacco Control (FCTC) provides comprehensive guidelines to restrict tobacco advertising, promotion, and sponsorship in digital spaces, including social media.¹⁸ Yet, evidence shows that tobacco promotion and advertising remain prevalent on member states’ digital media platforms.^19,20 To strengthen the enforcement of these policies, computer vision models can be used to detect the presence of tobacco marketing. A current limitation, however, is that most models are trained to identify only specific tobacco products.^11,12,15 Future research should focus on integrating these disparate models to allow for identification and differentiation both within and across product types.

We acknowledge the limitations of this study. We detected the presence of only large cigars. However, there are other accessories (eg, a cigar cutter, containers such as a humidor) that could also be used to identify cigars. We also used high-resolution images from Reddit. Although images obtained from different social media platforms are unlikely to affect model performance, future studies should assess the ability of these models to detect large cigar images across other social media platforms, as well as low-resolution images and cigar accessories. Identifying accessories is important, as they may be used to promote and contribute to large cigar use. Similarly, modified or blurred images of large cigars could also function as promotional tools.

A strength of our study is the inclusion of items that are difficult to distinguish from large cigars in our training data, which increased the robustness of the model. However, other commonly used items may share similar shapes, and future studies should examine whether large cigars can be reliably detected among a broader range of both similar and dissimilar products.

Public Health Implications

Our automated, scalable computer vision model represents a significant advancement in public health surveillance by enabling the identification of large cigars on social media. Leveraging this technology can strengthen surveillance efforts in digital spaces, where promotional activities frequently evade traditional regulatory measures.

Author Contributions

Grace Kong (Conceptualization [lead], Data curation, Formal analysis [supporting], Funding acquisition [lead], Investigation, Methodology [equal], Project administration, Resources [lead], Software [supporting], Supervision [lead], Validation, Visualization [supporting], Writing—original draft, Writing—review & editing [lead]), Shuvam Keshari (Conceptualization [supporting], Data curation, Formal analysis [lead], Investigation, Methodology, Project administration, Software [supporting], Validation, Visualization [lead], Writing—review & editing [supporting]), Rachel Ouellette (Conceptualization, Investigation [supporting], Writing—review & editing [equal]), Juhan Lee (Investigation, Project administration, Writing—review & editing [supporting]), and Dhiraj Murthy (Conceptualization, Data curation [lead], Formal analysis [supporting], Investigation [equal], Methodology, Project administration [lead], Resources [equal], Software [lead], Supervision, Validation [equal], Visualization [supporting], Writing—review & editing [equal])

Funding

Research reported in this publication was supported by National Institute on Drug Abuse (NIDA) and FDA Center for Tobacco Products (CTP) grant R01DA049878 (principal investigator: Kong, G). The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH or the Food and Drug Administration.

Declaration of Interests

The authors do not have any conflict of interests to declare.

Data Availability

The data used for this report will be shared upon reasonable request to the corresponding author.