Improving Suicidal Ideation Detection in Social Media Posts: Topic Modeling and Synthetic Data Augmentation Approach
Our results demonstrate that synthetic datasets can be useful to obtain an enriched understanding of online suicide discussions as well as build more accurate machine learning models for suicidal narrative detection on social media.
Authors
- Isar Nejadgholi (ORCID: https://orcid.org/0000-0001-6241-6114)
- Hamideh Ghanadian (ORCID: https://orcid.org/0000-0002-5203-3504)
- Hussein Al Osman (ORCID: https://orcid.org/0000-0002-7189-5644)
Institutions
- University of Ottawa (CA)
- National Research Council Canada (CA)
Publication Details
- Journal
- JMIR Formative Research
- Published
- 2025-05-08
- DOI
- https://doi.org/10.2196/63272
- Citations
- 1
- Primary Topic
- Suicide and Self-Harm Studies
- Type
- article
- Field-Weighted Citation Impact
- 2.31
Funders
- University of Ottawa
- Natural Sciences and Engineering Research Council of Canada