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Photographic Misrepresentation on Instagram After Facial Cosmetic Surgery: Is Increased Photography Bias Associated
Elbert E Vaca, Megan M Perez1, Jonathan B Lamano1
1Northwestern Feinberg School of Medicine, Chicago, IL, USA.
Background:
Before-and-after images are commonly used on Instagram (Menlo Park, CA) to advertise aesthetic surgical treatments and are a powerful means of engaging prospective patients. Consistency between before-and-after images accurately demonstrating the postoperative result on Instagram, however, has not been systematically assessed.
Objectives:
The aim of this study was to systematically assess facial cosmetic surgery before-and-after photography bias on Instagram.
Methods:
The authors queried 19 Instagram facial aesthetic surgery-related hashtags on 3 dates in May 2020. The "top" 9 posts associated with each hashtag (291 posts) were analyzed by 3 plastic surgeons by means of a 5-item rubric quantifying photographic discrepancies between preoperative and postoperative images. Duplicate posts and those that did not include before-and-after images of facial aesthetic surgery procedures were excluded.
Results:
A total of 3,477,178 posts were queried. Photography conditions were observed to favor visual enhancement of the postoperative result in 282/291 analyzed top posts, with an average bias score of 1.71 [1.01] out of 5. Plastic surgeons accounted for only 27.5% of top posts. Physicians practicing outside their scope of practice accounted for 2.8% of top posts. Accounts with a greater number of followers (P = 0.017) and posts originating from Asia (P = 0.013) were significantly associated with a higher postoperative photography bias score.
Conclusions:
Photographic misrepresentation, with photography conditions biased towards enhancing the appearance of the postoperative result, is pervasive on Instagram. This pattern was observed across all physician specialties and raises significant concerns. Accounts with a greater number of followers demonstrated significantly greater postoperative photography bias, suggesting photographic misrepresentation is rewarded by greater user engagement.
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