Epicosm：一种将在线社交媒体链接到流行病学队列的框架。
Epicosm-a framework for linking online social media in epidemiological cohorts.
机构信息
Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK.
Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.
出版信息
Int J Epidemiol. 2023 Jun 6;52(3):952-957. doi: 10.1093/ije/dyad020.
MOTIVATION
Social media represent an unrivalled opportunity for epidemiological cohorts to collect large amounts of high-resolution time course data on mental health. Equally, the high-quality data held by epidemiological cohorts could greatly benefit social media research as a source of ground truth for validating digital phenotyping algorithms. However, there is currently a lack of software for doing this in a secure and acceptable manner. We worked with cohort leaders and participants to co-design an open-source, robust and expandable software framework for gathering social media data in epidemiological cohorts.
IMPLEMENTATION
Epicosm is implemented as a Python framework that is straightforward to deploy and run inside a cohort's data safe haven.
GENERAL FEATURES
The software regularly gathers Tweets from a list of accounts and stores them in a database for linking to existing cohort data.
AVAILABILITY
This open-source software is freely available at [https://dynamicgenetics.github.io/Epicosm/].
动机
社交媒体为流行病学队列提供了一个无与伦比的机会，可以收集大量关于心理健康的高分辨率时间过程数据。同样，流行病学队列所拥有的高质量数据也可以极大地受益于社交媒体研究，成为验证数字表型算法的真实数据来源。然而，目前缺乏以安全和可接受的方式做到这一点的软件。我们与队列负责人和参与者合作，共同设计了一个开源的、强大的和可扩展的软件框架，用于在流行病学队列中收集社交媒体数据。
实现
Epicosm 是用 Python 框架实现的，它可以轻松地在队列的数据安全区内部署和运行。
通用特性
该软件定期从一系列账户中收集推文，并将其存储在数据库中，以便与现有队列数据建立链接。
可用性
这个开源软件可在 [https://dynamicgenetics.github.io/Epicosm/] 免费获得。
Zotero 插件