Using Natural Language Processing to Describe the Use of an Online Community for Abortion During 2022: Dynamic Topic Modeling Analysis of Reddit Posts
Notice bibliographique
Résumé
BACKGROUND: Abortion access in the United States has been in a state of rapid change and increasing restriction since the Dobbs v Jackson Women's Health Organization decision from the US Supreme Court in June 2022. With further constraints on access to abortion since Dobbs, the internet and online communities are playing an increasingly important role in people's abortion trajectories. There is a need for a broader understanding of how online resources are used for abortion and how they may reflect changes in the sociopolitical and legal context of abortion access. Research using online information and leveraging methods to work efficiently with large textual datasets has the potential to accelerate knowledge generation and provide novel insights into changing abortion-related experiences following Dobbs, helping address these knowledge gaps. OBJECTIVE: This project sought to use natural language processing techniques, specifically topic modeling, to explore the content of posts to 1 online community for abortion (r/abortion) in 2022 and assess how community use changed during that time. METHODS: This analysis described and explored posts shared throughout 2022 and for 3 subperiods of interest: before the Dobbs leak (December 24, 2021-May 1, 2022), Dobbs leak to decision (May 2, 2022-June 23, 2022), and after the Dobbs decision (June 24, 2022-December 23, 2022). We used topic modeling to obtain descriptive topics for the year and each subperiod and then classified posts. Topics were then aggregated into conceptual groups based on a combination of quantitative and qualitative assessments. The proportion of posts classified in each conceptual group was used to assess change in community interests across the 3 study subperiods. RESULTS: The 7273 posts shared in r/abortion in 2022 included in our analyses were categorized into 8 conceptual groups: abortion decision-making, navigating abortion access barriers, clinical abortion care, medication abortion processes, postabortion physical experiences, potential pregnancy, and self-managed abortion processes. Posts related to navigating access barriers were most common. The proportion of posts about abortion decision-making and self-management changed significantly across study periods (P=.006 and P<.001, respectively); abortion decision-making posts were more common before the Dobbs leak, whereas those related to self-management increased following the leak and decision. CONCLUSIONS: This analysis provides a holistic view of r/abortion posts in 2022, highlighting the important role of online communities as abortion-supportive online resources and changing interests among posters with abortion policy changes. As policies and pathways to abortion access continue to change across the United States, approaches leveraging natural language processing with sufficiently large samples of textual data present opportunities for timely monitoring, with the potential to reflect a broad range of abortion experiences, including those of people who have limited or no interaction with clinical abortion care.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».