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Protecting, promoting, and supporting breastfeeding on Instagram.

Alessandro R MarconMark BieberMeghan B Azad
Published in: Maternal & child nutrition (2018)
Breastfeeding has many established benefits for mothers, children, and society at large; however, the vast majority of infants globally do not meet international breastfeeding recommendations. There are many complex reasons for suboptimal breastfeeding rates, including social and societal factors. Alongside increasing social media use worldwide, there is an expanding research focus on how social media use affects health behaviours, decisions and perceptions. The objective of this study was to systematically determine if and how breastfeeding is promoted and supported on the popular social media platform Instagram, which currently has over 700 million active users worldwide. To assess how Instagram is used to depict and portray breastfeeding, and how users share perspectives and information about this topic, we analysed 4,089 images and 8,331 corresponding comments posted with popular breastfeeding-related hashtags (#breastfeeding, #breastmilk, #breastisbest, and #normalizebreastfeeding). We found that Instagram is being mobilized by users to publicly display and share diverse breastfeeding-related content and to create supportive networks that allow new mothers to share experiences, build confidence, and address challenges related to breastfeeding. Discussions were overwhelmingly positive and often highly personal, with virtually no antagonistic content. Very little educational content was found, contrasted by frequent depiction and discussion of commercial products. Thus, Instagram is currently used by breastfeeding mothers to create supportive networks and could potentially offer new avenues and opportunities to "normalize," protect, promote, and support breastfeeding more broadly across its large and diverse global online community.
Keyphrases
  • social media
  • preterm infants
  • health information
  • healthcare
  • mental health
  • public health
  • machine learning
  • deep learning
  • risk assessment
  • single cell
  • human health