在社交媒体上对物质使用模式的大规模深度学习启用的信息学分析:从COVID-19流行病的见解
Julina Maharjan1, Jianfeng Zhu1, Jennifer King2
1Department of Computer Science, Kent State University, Kent, OH, United States.
JMIR infodemiology
|April 17, 2025
概括
社交媒体分析显示,在COVID-19大流行期间,由于压力而增加了物质使用 (SU). 这项研究强调了实时SU趋势检测的潜力,以告知公共卫生干预措施.
科学领域:
- 数字流行病学数字流行病学
- 公共卫生监督是对公共卫生的监督.
- 计算社会科学 计算社会科学
背景情况:
- COVID-19大流行加剧了心理健康和物质使用 (SU) 的挑战.
- 增加的社会压力导致更高的药物依赖和过量服用率.
- 社交媒体的参与激增,提供了对公共卫生趋势的见解.
研究的目的:
- 在COVID-19大流行期间使用大规模社交媒体数据分析SU模式.
- 检查流行病前,流行病前和流行病后的时期,以进行基线和后果分析.
- 提供对各种药物类型和潜在主题的 SU 趋势的全面了解.
主要方法:
- 使用深度学习模型 (RoBERTa) 分析了113亿条推特帖子 (2019-2021).
- 采用了人为循环策略,以提高模型性能和数据注释.
- 应用统计技术 (趋势分析,聚类,主题建模) 并集成实时监控系统.
主要成果:
- 在2019年至2021年期间,确定了900万个SU相关职位.
- 观察到2020年疫情宣布后3天内,SU职位的显著增加21%.
- 酒精,大麻素和处方药的讨论越来越多,与COVID-19,心理健康和经济压力有关.
结论:
- 社交媒体数据可以在全球危机期间实时检测SU趋势.
- 心理健康和经济压力是SU峰值的关键驱动因素,特别是在酒精和处方药方面.
- 数据驱动的洞察力支持针对弱势群体的积极公共卫生干预措施.
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