A framework for linking and sharing social media data for high-resolution longitudinal measurement of mental health across CLOSER cohorts

用于链接和共享社交媒体数据的框架，用于对 CLOSER 队列中的心理健康进行高分辨率纵向测量

• 批准号: ES/R011583/1
• 负责人: Oliver Davis
• 金额: $ 13.31万
• 依托单位: University of Bristol
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2018
• 资助国家: 英国
• 起止时间: 2018 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=ES%2FR011583%2F1
• 关键词: framework; linking; sharing; social; media

Interactions on social media have the potential to help us to understand human behaviour, including the development of both good and poor mental health. However, to do the best science we need to know as much as possible about the people who are participating in our research. The CLOSER group of UK longitudinal cohorts include people who have contributed their data to research since birth. By inviting participants in these cohorts to also allow us to derive information from their social media feeds, we will be able to relate this information to gold-standard measures of the behaviours we are trying to understand and to world-class data on other aspects of life. To work out the best way to do this, our project will engage with participants in the Children of the '90s cohort to find out what is acceptable to them in terms of collecting and using their interactions on social media. We will use what we have learnt to develop software that collects and codes social media data in a way that protects the anonymity of participants by scoring Tweets without making the text available to researchers. We will share this software with other CLOSER cohorts to make it easy for them to invite participants to contribute their Twitter data in a safe and secure way. The high-resolution data collected in this way will help us to understand human behaviour and how mental health changes over time. Collecting these data in well known groups of people will also give scientists the information they need to improve the quality of all research using social media.

社交媒体上的互动有可能帮助我们了解人类行为，包括良好和不良心理健康的发展。然而，为了做最好的科学研究，我们需要尽可能多地了解参与我们研究的人。英国纵向队列的CLOSER组包括自出生以来就为研究贡献数据的人。通过邀请这些群体中的参与者也允许我们从他们的社交媒体馈送中获取信息，我们将能够将这些信息与我们试图理解的行为的黄金标准衡量标准以及生活其他方面的世界级数据联系起来。为了找到最好的方法，我们的项目将与90年代儿童群体的参与者进行接触，以了解他们在收集和使用社交媒体上的互动方面可以接受什么。我们将利用我们所学到的知识开发软件，收集和编码社交媒体数据，通过对推文进行评分而不向研究人员提供文本来保护参与者的匿名性。我们将与其他CLOSER群组共享此软件，以便他们能够轻松邀请参与者以安全可靠的方式贡献他们的Twitter数据。以这种方式收集的高分辨率数据将帮助我们了解人类行为以及心理健康如何随时间变化。在知名人群中收集这些数据也将为科学家提供他们需要的信息，以提高使用社交媒体的所有研究的质量。

项目成果

Methodologies for Monitoring Mental Health on Twitter: Systematic Review.

• DOI: 10.2196/42734
• 发表时间: 2023-05-08
• 期刊: JOURNAL OF MEDICAL INTERNET RESEARCH
• 影响因子: 7.4
• 作者: Cara, Nina H. Di;Maggio, Valerio;Davis, Oliver S. P.;Haworth, M. A.
• 通讯作者: Haworth, M. A.

Views on social media and its linkage to longitudinal data from two generations of a UK cohort study.

对社交媒体的看法及其与英国两代队列研究的纵向数据的联系。

• DOI: 10.12688/wellcomeopenres.15755.2
• 发表时间: 2020
• 期刊: Wellcome open research
• 作者: Di Cara NH