Peer-reviewed article
Use of machine learning methods to understand discussions of female genital mutilation/cutting on social media.
Babbs G, Weber SE, Abdalla SM, Cesare N, Nsoesie EO ·
Summary
Written by AI, not yet AICS's view · claude-haiku-4-5-20251001This study used machine learning to analyze Twitter discussions about female genital mutilation/cutting (FGM/C) from 2015 to 2020 across English-speaking countries. Researchers identified major discussion themes, tracked conversation volumes and geographic shifts, and found that most online conversation advocates for ending the practice. The study reveals that discussion patterns changed over time, with conversation moving from Western countries to Nigeria and Kenya by 2020, and highlights the importance of understanding cultural differences in how FGM/C is discussed for designing public health communication.
Key points
- Tweet volume about FGM/C remained relatively stable from 2015 to 2020
- Geographic shift in conversation occurred: from US and UK dominance through 2017 to Nigeria and Kenya in 2020
- Major discussion themes included Islam, International Day of Zero Tolerance, education, activism, human rights, and feminism
- The majority of English-speaking Twitter conversation advocated for ending FGM/C
- Tweet length and follower count were strong predictors of user engagement
Limitations
The study only examines English-language Twitter discussions, which may not represent the full diversity of public opinion on FGM/C or capture discussions in other languages or on other platforms. The analysis describes what is discussed online rather than measuring actual attitudes, beliefs, or behaviours related to FGM/C in communities where it is practised.
Abstract
Female genital mutilation/cutting (FGM/C) describes several procedures that involve injury to the vulva or vagina for nontherapeutic reasons. Though at least 200 million women and girls living in 30 countries have undergone FGM/C, there is a paucity of studies focused on public perception of FGM/C. We used machine learning methods to characterize discussion of FGM/C on Twitter in English from 2015 to 2020. Twitter has emerged in recent years as a source for seeking and sharing health information and misinformation. We extracted text metadata from user profiles to characterize the individuals and locations involved in conversations about FGM/C. We extracted major discussion themes from posts using correlated topic modeling. Finally, we extracted features from posts and applied random forest models to predict user engagement. The volume of tweets addressing FGM/C remained fairly stable across years. Conversation was mostly concentrated among the United States and United Kingdom through 2017, but shifted to Nigeria and Kenya in 2020. Some of the discussion topics associated with FGM/C across years included Islam, International Day of Zero Tolerance, current news stories, education, activism, male circumcision, human rights, and feminism. Tweet length and follower count were consistently strong predictors of engagement. Our findings suggest that (1) discussion about FGM/C has not evolved significantly over time, (2) the majority of the conversation about FGM/C on English-speaking Twitter is advocating for an end to the practice, (3) supporters of Donald Trump make up a substantial voice in the conversation about FGM/C, and (4) understanding the nuances in how people across cultures refer to and discuss FGM/C could be important for the design of public health communication and intervention.