This is an applied resource examining the concept of gendered health misinformation and how it may present to online users. We reviewed claims on social media, interviewed health experts, and consulted existing literature in order to put together a set of prioritized gendered health misinformation topics.
Gendered misinformation here is defined as the unintentional spread of false or substandard information about women, trans people, and nonbinary people. This is different from gendered disinformation, which is the intentional and coordinated spread of sexist information, primarily focused on women. The concept has yet to be formally conceptualized and is not widely researched, understood, or prioritized. In this report we focus on three nonexhaustive topics that capture a range of gendered health misinformation claims.
This resource captures examples of gendered health misinformation online. More research needs to be done to understand relative risks of gendered health misinformation narratives, as well as effective content moderation approaches to mitigating the harm of gendered health misinformation in online ecosystems.
The claims presented in this resource are examples of misinformation that are misleading, missing context, or outright false. Names and identifying characteristics of the users posting the misinformation have been blurred to protect their identities and reduce risks of harm and harassment to those accounts.
This primer was developed with contributions from topic area and field experts including Lauren Graybill, Elizabeth Pleasants, Anna Wexler, Laura Dodge, and with input from Meedan team members.
Meedan partners with media organizations, NGOs, and technology platforms— including some mentioned in this report—to support global journalism. Details about Meedan’s funding and collaborations are available in its annual reports. Meedan’s research is conducted independently of our funders.
- Online conversations are heavily influenced by news coverage, like the 2022 Supreme Court decision on abortion. The relationship is less clear between big breaking news and specific increases in online misinformation.
- The tweets analyzed were a random sample qualitatively coded as “misinformation” or “not misinformation” by two qualitative coders trained in public health and internet studies.
- This method used Twitter’s historical search API
- The peak was a significant outlier compared to days before it using Grubbs' test for outliers for Chemical Abortion (p<0.2 for the decision; p<0.003 for the leak) and Herbal Abortion (p<0.001 for the decision and leak).
- All our searches were case insensitive and could match substrings; so, “revers” matches “reverse”, “reversal”, etc.
Jenna Sherman, MPH, is a Program Manager for Meedan’s Digital Health Lab. Her work has focused on digital health challenges across information access, maternal incarceration, and discrimination in AI. She has her MPH from the Harvard T.H. Chan School of Public Health in Social and Behavioral Sciences.