Epicosm—a framework for linking online social media in epidemiological cohorts
International Journal of Epidemiology(2023)
摘要
Abstract 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/].
更多查看译文
关键词
online social media,social media,epicosm—a
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要