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

被引:0
|
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.
引用
收藏
页数:14
相关论文
共 50 条
  • [1] What can social media tell us about the opioid crisis in Canada?
    Tibebu, Semra
    Chang, Vicky C.
    Drouin, Charles-Antoine
    Thompson, Wendy
    Do, Minh T.
    HEALTH PROMOTION AND CHRONIC DISEASE PREVENTION IN CANADA-RESEARCH POLICY AND PRACTICE, 2018, 38 (06): : 263 - 267
  • [2] What social capital can tell us about social presence
    Oztok, Murat
    Zingaro, Daniel
    Makos, Alexandra
    BRITISH JOURNAL OF EDUCATIONAL TECHNOLOGY, 2013, 44 (06) : E203 - E206
  • [3] What can music tell us about social interaction?
    D'Ausilio, Alessandro
    Novembre, Giacomo
    Fadiga, Luciano
    Keller, Peter E.
    TRENDS IN COGNITIVE SCIENCES, 2015, 19 (03) : 111 - 114
  • [4] What social determinants can tell us about schizophrenia
    Malaspina, Dolores
    SCHIZOPHRENIA RESEARCH, 2023, 256 : 114 - 116
  • [5] What Can Twitter Tell Us about Skin Cancer Communication and Prevention on Social Media?
    Vasconcelos Silva, Carina
    Jayasinghe, Dilki
    Janda, Monika
    DERMATOLOGY, 2020, 236 (02) : 81 - 89
  • [6] WHAT CAN COST ANALYSES TELL US
    HUNGATE, TL
    NURSING OUTLOOK, 1960, 8 (04) : 191 - 191
  • [7] WHAT CAN COTD ANALYSES TELL US?
    Roschnik, Natalie
    Schofield, Lilly
    ANNALS OF NUTRITION AND METABOLISM, 2017, 71 : 252 - 252
  • [8] What the social brain sciences can tell us about the self
    Heatherton, TF
    Macrae, CN
    Kelley, WM
    CURRENT DIRECTIONS IN PSYCHOLOGICAL SCIENCE, 2004, 13 (05) : 190 - 193
  • [9] The crisis in economics: What can it tell us about social science?
    Hammersley, Martyn
    CONTEMPORARY SOCIAL SCIENCE, 2014, 9 (03) : 338 - 344
  • [10] What can cutans tell us about?
    T. A. Sokolova
    Eurasian Soil Science, 2008, 41 (1) : 102 - 104