The digital epidemiology of social media behaviour and mental wellbeing in the Avon Longitudinal Study of parents and children

最近，报告的心理健康问题出现了惊人的上升，尤其是在年轻人中。这意味着了解心理健康和福祉的起源比以往任何时候都更加重要，以便为公共卫生干预措施和政府政策提供信息，以促进良好的心理健康并预防精神疾病的深远后果，这是政府和主要研究资助者都认识到的紧迫性。社交媒体和数字足迹的其他方面具有巨大潜力，可以通过获取关于人类行为的生态学上有效的实时数据，促进对精神疾病原因的研究。例如，文献包括检测心理健康状况的自动化方法的例子，如抑郁症、创伤后应激障碍、自杀意念和季节性情感障碍。这些来自社交媒体的测量有可能补充这些维度和障碍的传统问卷调查和诊断性访谈测量，因为它们基于大量真实的社会互动，而不依赖于回顾性回忆或内省。根据实际情况验证这些数字表型方法是实现这一潜力的关键。然而，社交媒体研究的参与者通常是不知名的，因此从历史上看，很难有效地做到这一点，这一限制迄今为止限制了使用社交媒体和数字足迹的其他方面进行研究的有用性。为了克服这个问题，最近的努力将英国出生队列的社交媒体数据联系起来，比如雅芳父母和孩子的纵向研究（ALSPAC）。在具有良好特征的人群中研究数字表型，并采用既定的基本事实测量方法，使我们能够将社交媒体分析置于可靠的流行病学基础之上。该项目旨在通过使用来自ALSPAC的约900名参与者的链接Twitter数据进行分析，解决早期研究的局限性。这些数据将用于探索推特行为（例如，一天中发推文的时间、推文中使用的语言、与其他用户的互动）与心理健康和福祉之间的关联。在可能的情况下，我们将使用最先进的方法对这些关系进行定向因果推断。在方法上，这将涉及机器学习技术，包括自然语言处理和因果推理方法，如格兰杰因果关系和孟德尔随机化。这个项目可以加强对社交媒体和心理健康结果之间关系的研究。这可以探索社交媒体与心理健康之间的各种因果机制，并确定潜在有害的社交媒体使用。这可以为旨在改变社交媒体行为的干预措施的发展提供信息。此外，了解这些关系可以帮助提高旨在从社交媒体上的行为预测心理健康状况的机器学习模型的鲁棒性。使用Twitter的时间与心理健康结果之间的关系是什么？Twitter网络特征与心理健康结果之间的关系是什么？收到的Twitter内容和心理健康结果之间的关系是什么

There have been recent and startling rises in reported mental health difficulties, particularly in young adults. This means it is more crucial than ever to understand the origins of mental health and wellbeing to inform public health interventions and governmental policies that promote good mental health and prevent the far-reaching consequences of mental illness, an urgency recognised by both government and major research funders. Social media and other aspects of digital footprint have huge potential to facilitate the study of the causes of mental ill health through access to ecologically valid real time data on human behaviour. For example, the literature includes examples of automated methods for the detection of mental health conditions such as depression, Post-Traumatic Stress Disorder, suicidal ideation and Seasonal Affective Disorder. These measurements from social media have the potential to complement traditional questionnaire and diagnostic interview measures of these dimensions and disorders because they are based on large numbers of real social interactions and do not rely on retrospective recall or introspection.Validating these digital phenotyping approaches against ground truth is key to realising this potential. However, participants in social media research are usually unknown, so it has been historically difficult to do this effectively, a limitation that has so far restricted the usefulness of research using social media and other aspects of the digital footprint. To overcome this, recent efforts have linked social media data in UK birth cohorts such as the Avon Longitudinal Study of Parents and Children (ALSPAC). Studying digital phenotypes in well characterised populations with established ground truth measures allows us to put social media analysis on a sound epidemiological footing.This project aims to address limitations of earlier research by performing analysis using linked Twitter data for approximately 900 participants from ALSPAC. These data will be used to explore associations between Twitter behaviours (e.g. time of day when tweeting, language used in tweets, interactions with other users) and mental health and well-being. Where possible, we will use state-of-the-art approaches to make directional causal inferences about these relationships. Methodologically, this will involve machine learning techniques including Natural Language Processing and causal inference methods such as Granger Causality and Mendelian Randomization.This project could enhance research into the relationship between social media and mental health outcomes. This could enable exploration of the various proposed causal mechanisms between social media and mental health, and the identification of potentially harmful social media usage. This could inform the development of interventions aiming to modify social media behaviour. Furthermore, knowledge of these relationships could help improve the robusticity of machine learning models aiming to predict mental health status from behaviour on social mediaThis project has three main research questions:1. What is the relationship between timing of Twitter activity and mental health outcomes?2. What is the relationship between Twitter network characteristics and mental health outcomes?3. What is the relationship between incoming Twitter content and mental health outcomes