What social media analyses can tell us about Ghanaian women's concerns during pregnancy

被引:0
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作者
Anto-Ocrah, Martina [1 ,2 ]
Valachovic, Tori [3 ]
Lanning, Joseph W. [4 ]
Ghanem, Ali [5 ]
Couturier, Claire [6 ,7 ]
Hakizimana, Celestin [8 ]
Niyomugabo, Celestin [8 ]
Affan, Nabeeha Jabir [2 ]
Vempalli, Hemika [1 ]
Kodam, Ruth Sally [9 ]
机构
[1] Univ Pittsburgh, Sch Med, Div Gen Internal Med, Pittsburgh, PA 15213 USA
[2] Univ Pittsburgh, Sch Publ Hlth, Dept Epidemiol, Pittsburgh, PA 15260 USA
[3] Univ Rochester, Sch Med, Rochester, NY USA
[4] Sch Int Training Grad Inst, Sustainable Dev Practice, Brattleboro, VT USA
[5] UT Southwestern Med Ctr, Dept Neurol, Dallas, TX USA
[6] Univ Pittsburgh, Sch Hlth & Rehabil Sci, Pittsburgh, PA USA
[7] Peace Corps, Conakry, Guinea
[8] VONSUNG, Kigali, Rwanda
[9] MidWife Sally Org, Dawhenya, Ghana
来源
关键词
pregnancy; Ghana; Africa; social media; machine learning; Facebook; PHYSICAL-ACTIVITY; SEEKING; ONLINE; HEALTH;
D O I
10.3389/fdgth.2025.1479392
中图分类号
R19 [保健组织与事业(卫生事业管理)];
学科分类号
摘要
Introduction Social media platforms are used by over 4.9 billion people for networking and community building, as well as for healthcare information seeking and decision-making. Most studies investigating the utilization of social media during pregnancy have focused on high-income countries, despite the surge in social media utilization globally. In this study, we analyzed how pregnant women in Ghana, West Africa, utilize Facebook to inform decision-making on their most salient pregnancy concerns.Methods We utilized machine learning techniques (Web Scraping and Latent Dirichlet Allocation) to mine and analyze posts from the Ghana-based MidWife Sally Pregnancy School Facebook group between August 16, 2020 and April 29, 2023. Posts were extracted, cleaned, and analyzed using Gensim python library. Topics were generated based on their probabilities and relevance to the study goal.Results A total of 3,328 posts were extracted and 3,322 were analyzed after removing 6 empty posts. Seven major topics with listed subtopics were identified: Pregnant (693 posts): personal physiological changes, exercise during pregnancy, medication (e.g., anti-malarials, pain killers) Delivery (367): emergency delivery, vaginal/caesarean birthing, breastmilk production, exercise during pregnancy Pain (350): location of pain and pain relief modalities (e.g., exercise, medication, sleep) Breastfeeding (248): delivery, emergency service, milk production Water (174): cold water consumption, infant feeding (e.g., gripe water, constipation, formula) Sleeping (165): discomfort, sleeping positions, exercise to induce sleep, sleep as a natural analgesic Antenatal (124): fetal growth, progress, hospital selection Of note, content from "Pregnant", "Delivery" and "Sleeping" included mentions of depression, while "Breastfeeding" highlighted cultural approaches to increasing milk production. The sentiment analysis showed that 43.4% of the responses were neutral and primarily focused on seeking information. Negative sentiments, which were more distressing, comprised 46.4% of the responses, while positive sentiments, had a celebratory tone and represented 10.2% of the data.Conclusion Social media analysis, previously employed in high income settings, can provide impactful, granular snapshots of pregnant people's concerns in the African region, which could be used to inform social media interventions aimed at filling educational gaps in antenatal care for those without adequate healthcare access.
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页数:14
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