Protecting, promoting, and supporting breastfeeding on Instagram
Bibliographic record
Abstract
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".