Large-Scale Deep Learning–Enabled Infodemiological Analysis of Substance Use Patterns on Social Media: Insights From the COVID-19 Pandemic
This study demonstrates the potential of leveraging social media data for real-time detection of SU trends during global crises. By uncovering how factors such as mental health and economic stress drive SU spikes, particularly in alcohol and prescription medication, we offer crucial insights for public health strategies. Our approach paves the way for proactive, data-driven interventions that will help mitigate the impact of future crises on vulnerable populations.
Authors
- Deric R. Kenne (ORCID: https://orcid.org/0000-0003-2852-5776)
- NhatHai Phan (ORCID: https://orcid.org/0000-0002-1032-8275)
- Jennifer King (ORCID: https://orcid.org/0009-0002-3794-5828)
- Julina Maharjan (ORCID: https://orcid.org/0009-0003-3390-0259)
- Jianfeng Zhu (ORCID: https://orcid.org/0000-0003-4779-3696)
- Ruoming Jin (ORCID: https://orcid.org/0000-0003-1895-4243)
Institutions
- New Jersey Institute of Technology (US)
- Kent State University (US)
Publication Details
- Journal
- JMIR Infodemiology
- Published
- 2025-03-02
- DOI
- https://doi.org/10.2196/59076
- Citations
- 7
- Primary Topic
- Health, Environment, Cognitive Aging
- Type
- article
- Field-Weighted Citation Impact
- 7.20