使用社交媒体指标预测危机中的公共心理健康需求:新加坡的大数据研究
Nur Atiqah Othman1, Chitra Panchapakesan1, Siyuan Brandon Loh1
1Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR), 1 Fusionopolis Way, #16-16 Connexis, Singapore, 138632, Republic of Singapore.
Scientific reports
|October 5, 2024
概括
来自Twitter的社交媒体情绪指标可以帮助预测紧急心理健康需求. 这项研究表明,分析推特上的公众情绪为预测对心理健康服务需求提供了有价值的工具.
科学领域:
- 数字心理健康数字心理健康
- 计算社会科学 计算社会科学
- 公共卫生监督是对公共卫生的监督.
背景情况:
- 随着COVID-19的流行,全球精神健康问题显著增加.
- 现有的数据和工具不足以使卫生政策制定者能够预测心理健康需求,特别是在危机期间.
- 需要新的方法来监测和预测公共心理健康趋势.
研究的目的:
- 调查从Twitter帖子中算法测量情绪指标的实用性,用于预测紧急心理健康需求.
- 与传统的心理健康数据相比,评估社交媒体情绪的预测能力.
- 探索数字数据在实时公共心理健康监测中的潜力.
主要方法:
- 利用了来自新加坡的时间序列数据,涵盖了549天 (2020年7月1日至2021年12月31日).
- 通过每日访问主要精神病医院的急诊室和危机用户在政府在线门户网站上计算心理健康需求.
- 采用1至5天的滞后时间的Pairwise Granger因果测试来分析推特情绪指标 (快乐,愤怒,悲伤) 与心理健康需求指标.
主要成果:
- 结合推特衍生的情绪 (快乐,愤怒,悲伤) 的预测模型显著超过了仅使用过去心理健康数据的基线模型.
- 具体发现包括IMH访问的快乐强度 (χ2 = 14.9,P < .001) 和思维危机上的悲伤数量 (χ2 = 4.6,P = .031) 与一天的滞后.
- 这些结果表明,在Twitter上公开表达情绪和立即使用心理健康服务之间存在统计学上显著的关系.
结论:
- 来自Twitter等社交媒体平台的算法测量情绪指标显示出作为公共心理健康需求早期预警指标的巨大潜力.
- 整合数字数据流可以提高卫生政策制定者的预测和应对心理健康危机的能力.
- 这项研究强调了利用社交媒体分析为积极的公共心理健康监测和资源分配的价值.
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