A Tumblr thematic analysis of perinatal health: Where users go to seek support
Bibliographic record
Abstract
Abstract With the research sex gap impacting available data on women’s health and the growing popularity of social media, it is not rare that individuals will seek health-related information on such platforms. Understanding how women use social media for perinatal-specific issues is crucial to gain knowledge on specific needs and gaps. The Tumblr platform is an excellent candidate to further understand the representation and discourse regarding perinatal health on social media. The objective was to identify specific themes to assess the present discourse pertaining to perinatal health. Posts were collected using Tumblr’s official API client over a 4-day period, from August 18 to 21, 2023, inclusively. A sentiment analysis was performed using the Valence Aware Dictionary and sEntiment Reasoner sentiment analysis toolkit and a deductive thematic analysis. In total, 235 posts were analyzed, and 11 individual categories were identified and divided into two main concepts; Women’s Health (Endometriosis; Postpartum Depression, Menopause, Miscarriage, Other Health Problems, Political Discourse) and Pregnancy/Childbirth (Maternal Mortality, Personal Stories, Pregnancy Symptoms, and Fitness/diet/weight). The last category was classified as Misinformation/Advertisement. Findings revealed that users used the Tumblr platform to share personal experiences regarding pregnancy, seek support from others, raise awareness, and educate on women’s health topics. Misinformation represented only 3% of the total sample. The present study demonstrates the feasibility of using in-depth data from Tumblr posts to inform us regarding current issues and topics specific to perinatal and women’s health. More research studies are needed to better understand the impact of social support and misinformation on perinatal health.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".