Data collection

Stratified random sampling was used to select images from Instagram and Pinterest over a span of five weeks (N=1800 pictures). Images were selected from pinterest.com using a computer. Because the Instagram search function is only available on smartphones and tablets, an outside search tool (Statigram, which later became Iconosquare) was used to collect Instagram images via computer.

Twice a week at a random day and time (between 8 a.m. and 11 p.m.), nine different search terms were used to capture a total of 10 pictures from each social media tool, yielding 180 pictures per social media site a week. Originally, ten search terms, accounting for the largest number of posts, were assessed. However, images reached saturation by the ninth search term. Thus, the tenth most popular search term (i.e. electroniccigarettes) was removed because no new images were gathered as a result of this search; retaining it would have led to resampling/recoding of images.

In order to limit the impact of “spam posting” (when retailers flood search results with their advertisements) on the study sample, only 10 pictures were captured from each search term. This method limited overrepresentation of the same image with similar messages repeatedly posted by users. After five weeks a total of 900 pictures (n=100 for each search term) had been captured for each social media tool.

For both social media sites, the nine most popular search terms related to e-cigs were used. These terms, listed by the number of Instagram hits at the time of writing, are “vape” (8,200,037 hits), “vapelife” (4,945,461 hits), “vapor” (2,353,208 hits), “vapelyfe” (3,847,074 hits), “vapeporn” (4,397,348 hits), “ecig” (1,170,096 hits), “vaping” (2,253,321 hits), “ecigs” (53,466 hits), and “electroniccigarette” (47,577 hits).

Data analysis

After the images were selected, they were classified into 10 categories. The categories were generated using inductive category development¹⁹. During this process, researchers reviewed 100 photos and recorded phrases (or words) attributed to the image, as well as first reactions or perceived themes represented by the image. After 100 images were coded, words or phrases were grouped into emergent categories (for both Instagram and Pinterest). The categories were then reviewed and revised, and any disagreements were negotiated.

Once the revised 10 categories were finalized, coding was repeated for the first 100 photos to verify the categories were exhaustive. Then, each of the images identified in data collection for Pinterest and for Instagram was coded into one or more categories. During image coding past the first 100 photos, an eleventh category, not previously identified in the initial phase of categorization, was created. A small sample of images fit only into this novel category, marijuana.

This systemic approach allowed for creation of distinct categories while reducing overlap and redundancy. The categories that resulted from this approach were customization, juice/flavors, memes, marketing, celebrities, health benefits, anti-smoking, models, tricks, marijuana, and social acceptance. The categories are described briefly in the results section and the frequency of images in each category is noted.

Description of categories

One of the most popular categories was customization (50.8% of all images). Users frequently shared information on customizing, or modifying, e-cig devices for both functional and aesthetic purposes.

Numerous images were devoted to marketing e-cigs (53.1%). Such images were posted by retailers and manufacturers and designed to pique consumer interest in sampling or purchasing particular products.

Another category conveyed information on juice or flavors that can be used in e-cigs (9.72%). These images described popular and new flavors as well as gave ideas on creating novel flavors.

Some images displayed e-cig use by celebrities (3.11%). Through such postings, e-cigs are depicted as “hip” and “cool”, legitimizing their use.

In a related vein, some postings featured professional or amateur models using e-cigs (8.22%). Typically these images were seductive or provocative, and involved endorsing products, enticing use, and/or garnering attention.

Some images conveyed perceived health benefits associated with e-cig use (2.66%). These posts included personal experiences as well as manufacturer claims.

Further, some images touted e-cigs as a replacement for traditional cigarettes and asserted that e-cig use is better (4.83%). Beyond potential health benefits, economic and environmental savings were suggested.

A meme is a picture accompanied by text (e.g. catchphrase) that is shareable online and has the potential to go “viral”. On both Instagram and Pinterest, memes often served comedic purposes, while simultaneously delivering another message (7.67%).

Some users uploaded pictures of performing tricks (7.8%). These tricks consist of exhaling vapor in creative ways, such as producing vapor rings or blowing vapor out the mouth and sucking it back into the nose (“a waterfall”).

With cigarette use indoors and in public places often banned, images may be posted to convey social acceptance of e-cigs (e.g. convenient to use in cigarette restricted areas). It is likely that in the hope of attracting potential customers swayed by the social acceptance of e-cigs, companies posted these types of images (1.33%).

Some images dealt with marijuana use or the potential for such use in e-cig devices (2.0%). Often, these images shared updated technology for achieving the best “high” or experiences.

Image frequency by category

Similar findings emerged for Instagram and Pinterest (see Table 1). For example, the most popular image categories on both platforms were marketing and customization. Results for Instagram indicate the three most popular categories were marketing (60.11%), customization (37.67%) and juice/flavors (17.56%). On Pinterest, the three most popular were customization (64.00%), marketing (46.11%) and memes (10.22%).