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A Mixed-Methods Analysis of Women’s Health Misinformation on Social Media

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Human-Computer Interaction – INTERACT 2023 (INTERACT 2023)

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Abstract

Propelled by the COVID-19 pandemic and recent overturning of Roe vs. Wade in the United States [21], concerns have grown around the proliferation of reproductive health misinformation online. While a body of work in HCI has explored female health and wellbeing from a socio-technical perspective, a knowledge gap relating to women’s health misinformation and how it presents on social media remains. We report a mixed-methods content analysis of the ideological rhetoric, sources, and claims present in a sample of 202 officially fact-checked posts relating to female reproductive health. We found that reproductive health misinformation is diverse in its sources and represents a range of ideological standpoints, including pro-choice, feminist, and anti-authority rhetoric. We also found that claims are often tacit in nature, and rely on subtle manipulation and exaggerations to convey misleading narratives, as opposed to complete fabrications. In sum, we present a timely and nuanced analysis of the women’s health misinformation ecosystem. Our findings may inform priorities for HCI interventions that abate health misinformation, and more broadly, support women in navigating a complex and polarised information landscape.

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Notes

  1. 1.

    Refers to the mainstream Republican political party in the United States, otherwise known as the GOP.

References

  1. Almeida, T., Comber, R., Balaam, M.: HCI and intimate care as an agenda for change in women’s health. In: Proceedings of the CHI Conference on Human Factors in Computing Systems, pp. 2599–2611. ACM, San Jose (2016). https://doi.org/10.1145/2858036.2858187

  2. Almenar, E., Aran-Ramspott, S., Suau, J., Masip, P.: Gender differences in tackling fake news: different degrees of concern, but same problems. Media Commun. 9(1), 229–238 (2021). https://doi.org/10.17645/mac.v9i1.3523

    Article  Google Scholar 

  3. Appling, S., Bruckman, A., De Choudhury, M.: Reactions to Fact Checking. Proc. ACM Hum.-Comput. Interact. (2022). https://doi.org/10.1145/3555128. CSCW, Association for Computing Machinery, New York

  4. Ayers, J.W., Caputi, T.L., Nebeker, C., Dredze, M.: Don’t quote me: reverse identification of research participants in social media studies. NPJ Digit. Med. 1(1), 30 (2018). https://doi.org/10.1038/s41746-018-0036-2

    Article  Google Scholar 

  5. Baker, S.A., Walsh, M.J.: ‘A mother’s intuition: it’s real and we have to believe in it’: how the maternal is used to promote vaccine refusal on Instagram. Inf. Commun. Soc., 1–18 (2022). https://doi.org/10.1080/1369118X.2021.2021269

  6. Bardzell, S.: Feminist HCI: taking stock and outlining an agenda for design. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI ’10, pp. 1301–1310. Association for Computing Machinery, New York (2010). https://doi.org/10.1145/1753326.1753521

  7. Bhattacharya, O., Siddiquea, B.N., Shetty, A., Afroz, A., Billah, B.: COVID-19 vaccine hesitancy among pregnant women: a systematic review and meta-analysis. BMJ Open 12(8), e061477 (2022). https://doi.org/10.1136/bmjopen-2022-061477

    Article  Google Scholar 

  8. Bhuiyan, M.M., Horning, M., Lee, S.W., Mitra, T.: NudgeCred: supporting news credibility assessment on social media through nudges. Proc. ACM Hum.-Comput. Interact. 5(CSCW2), 1–30 (2021). https://doi.org/10.1145/3479571

    Article  Google Scholar 

  9. Cohen, M.: Towards a framework for women’s health. Patient Educ. Couns. 33(3), 187–196 (1998). https://doi.org/10.1016/S0738-3991(98)00018-4

    Article  Google Scholar 

  10. Dedrick, A., Merten, J.W., Adams, T., Wheeler, M., Kassie, T., King, J.L.: A content analysis of Pinterest belly fat loss exercises: unrealistic expectations and misinformation. Am. J. Health Educ. 51(5), 328–337 (2020). https://doi.org/10.1080/19325037.2020.1795754

    Article  Google Scholar 

  11. Ecker, U.K.H., et al.: The psychological drivers of misinformation belief and its resistance to correction. Nat. Rev. Psychol. 1(1), 13–29 (2022). https://doi.org/10.1038/s44159-021-00006-y, https://www.nature.com/articles/s44159-021-00006-y

  12. Flintham, M., Karner, C., Bachour, K., Creswick, H., Gupta, N., Moran, S.: Falling for fake news: investigating the consumption of news via social media. In: Proceedings of the CHI Conference on Human Factors in Computing Systems, pp. 1–10. Association for Computing Machinery, New York (2018). https://doi.org/10.1145/3173574.3173950

  13. Google: Google Fact Check Explorer - About (2023). https://toolbox.google.com/factcheck/about#fce

  14. Han, L., Boniface, E.R., Han, L.Y., Albright, J., Doty, N., Darney, B.G.: The abortion web ecosystem: cross-sectional analysis of trustworthiness and bias. J. Med. Internet Res. 22(10), e20619 (2020). https://doi.org/10.2196/20619

    Article  Google Scholar 

  15. HealthNews: period-blood face mask: why gynecologists disapprove? (2023). https://healthnews.com/beauty/skin-care/is-period-blood-good-for-skin-care/

  16. Jolly, N.: Why are women buying GOOP? Women’s health and the wellness movement. Birth 47(3), 254–256 (2020). https://doi.org/10.1111/birt.12495

    Article  Google Scholar 

  17. Lewandowsky, S., van der Linden, S.: Countering misinformation and fake news through inoculation and Prebunking. Eur. Rev. Soc. Psychol. 32, 348–384 (2021). https://doi.org/10.1080/10463283.2021.1876983

    Article  Google Scholar 

  18. McCammon, E., Bansal, S., Hebert, L.E., Yan, S., Menendez, A., Gilliam, M.: Exploring young women’s menstruation-related challenges in Uttar Pradesh, India, using the socio-ecological framework. Sex. Reprod. Health Matters 28(1), 1749342 (2020). https://doi.org/10.1080/26410397.2020.1749342

    Article  Google Scholar 

  19. Meta: how Meta’s third-party fact-checking program works (2021). https://www.facebook.com/formedia/blog/third-party-fact-checking-how-it-works

  20. OASH: A-Z Health Topics. https://www.womenshealth.gov/a-z-topics

  21. Pagoto, S.L., Palmer, L., Horwitz-Willis, N.: The next Infodemic: abortion misinformation. J. Med. Internet Res. 25, e42582 (2023). https://doi.org/10.2196/42582, https://www.jmir.org/2023/1/e42582

  22. Rougerie, P.: Abortifacient plants are dangerous, and ineffective as an abortion method (2022). https://healthfeedback.org/claimreview/abortifacient-plants-dangerous-and-ineffective-as-abortion-method/

  23. Saltz, E., Leibowicz, C.R., Wardle, C.: Encounters with visual misinformation and labels across platforms: an interview and diary study to inform ecosystem approaches to misinformation interventions. In: Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems, pp. 1–6. ACM, Yokohama (2021). https://doi.org/10.1145/3411763.3451807

  24. Shahi, G.K., Dirkson, A., Majchrzak, T.A.: An exploratory study of COVID-19 misinformation on Twitter. Online Soc. Netw. Media 22, 100104 (2021). https://doi.org/10.1016/j.osnem.2020.100104

    Article  Google Scholar 

  25. Shahid, F., Kamath, S., Sidotam, A., Jiang, V., Batino, A., Vashistha, A.: "It Matches My Worldview": examining perceptions and attitudes around fake videos. In: CHI Conference on Human Factors in Computing Systems, pp. 1–15. ACM, New Orleans (2022). https://doi.org/10.1145/3491102.3517646

  26. Sharevski, F., Loop, J.V., Jachim, P., Devine, A., Pieroni, E.: Abortion misinformation on TikTok: rampant content, lax moderation, and vivid user experiences (2023). http://arxiv.org/abs/2301.05128

  27. Simon, F., Howard, P.N., Nielsen, R.K.: Types, sources, and claims of COVID-19 misinformation. Technical report, Reuters Institute (2022)

    Google Scholar 

  28. Thomas, S.P.: Trust also means centering black women’s reproductive health narratives. Hastings Cent. Rep. 52(S1), S18–S21 (2022). https://doi.org/10.1002/hast.1362

    Article  MathSciNet  Google Scholar 

  29. Tuli, A., Chopra, S., Kumar, N., Singh, P.: Learning from and with Menstrupedia: towards menstrual health education in India. Proc. ACM Hum.-Comput. Interact. 2(CSCW), 1–20 (2018). https://doi.org/10.1145/3274443

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Correspondence to Lisa Mekioussa Malki .

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Malki, L.M., Patel, D., Singh, A. (2023). A Mixed-Methods Analysis of Women’s Health Misinformation on Social Media. In: Abdelnour Nocera, J., Kristín Lárusdóttir, M., Petrie, H., Piccinno, A., Winckler, M. (eds) Human-Computer Interaction – INTERACT 2023. INTERACT 2023. Lecture Notes in Computer Science, vol 14144. Springer, Cham. https://doi.org/10.1007/978-3-031-42286-7_22

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  • DOI: https://doi.org/10.1007/978-3-031-42286-7_22

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