Often Hyperconnected, Seldom understood

通常高度连接，但很少被理解

• 批准号: 2271816
• 金额: --
• 依托单位: University of Reading
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2271816
• 关键词: Often; Hyperconnected; Seldom; understood

Motivation and OverviewThe increasing proliferation of social media has led to a number of positive effects in supporting social engagement and cohesion as well as sharing insights, and, for some, a sense of belonging to same (sub) group or community of interest. The prevalence and ubiquity of social media linking as an "always-on" lifestyle whilst serving as a way to feel online togetherness, can paradoxically also become a route to feel loneliest and misunderstood due to various behaviours of participants such as insensitive , aggressive, deceptive and obsessive conduct meted out to the most vulnerable. Over the last decade much work has focused on analysis of patterns of behaviour and self-expression on the internet and its possible correlates including the increasing trend in mental illness, particularly amongst the adolescent and young adults as heavy users of social media.According to the World Health Organisation "Half of all mental illness begins by the age of 14, but most cases go undetected and untreated. In terms of the burden of the disease among adolescents, depression is the third leading cause, and, suicide is the second leading cause of death among 15-29-year-olds" https://www.who.int/mental_health/world-mental-health-day/2018/en/The proposed research study aim to address the analysis of undetected mental and/or physical illness based on the social media behaviours of users; particularly the lonely and the vulnerable who for some reason may be unable to articulate their real feelings in the physical or virtual space. However their patterns of presence, absence, participation, self-expression and verbal or written exchanges with others may help reveal physical or mental ill-health which could otherwise go undetected and may indeed be serious and need urgent attention.Thus the thesis contends that using machine learning and analytics techniques it should be possible to detect changes in the behaviour of the online persona of a user, as exhibited in terms of the above patterns, that may correlate with the onset or deterioration of mental or physical health such as suicidal thoughts, clinical depression, anxiety and obsessive disorders, or indeed the onset of sever life threatening infectious diseases such as meningitis, septicaemia etc.Data Assets & EthicsIn the first instance an ethically-guided approach to data assets engineering will ensure the acquisition of open data sources with data-subject identity anonymised at source. Over the life-cycle of the project it is possible that some synthetic data will be generated, based on established behavioural profiles for various personality disorders, for model building and testing purposes. MethodologyThis PhD study demands challenging and innovative deployment of various AI and Data Analytics techniques notably Metadata, Ontology and Privacy Engineering. Natural Language Processing, Spatio-Temporal Data Stream Mining, Uncertainty Modelling and Generative Networks. This study is funded through a studentship grant from EPSRC and the School of Mathematical, Statistical and Computational Sciences, under EPSRC Contract No

动机和概述社交媒体的日益普及在支持社会参与和凝聚力以及分享见解方面产生了许多积极影响，对一些人来说，对同一（子）群体或兴趣社区的归属感。社交媒体连接作为一种“永远在线”的生活方式的流行和无处不在，同时作为一种在线归属感的方式，也可能自相矛盾地成为一种最孤独和被误解的途径，因为参与者的各种行为，如对最脆弱的人不敏感、咄咄逼人、欺骗和强迫行为。在过去的十年中，许多工作都集中在分析互联网上的行为模式和自我表达及其可能的相关性，包括精神疾病的增加趋势，特别是在青少年和年轻人中，因为他们是社交媒体的重度用户。根据世界卫生组织的数据，“一半的精神疾病始于14岁，但大多数病例未被发现和治疗。就青少年的疾病负担而言，抑郁症是第三大原因，自杀是15-29岁人群死亡的第二大原因" https://www.who.int/mental_health/world-mental-health-day/2018/en/The拟议的研究旨在根据用户的社交媒体行为分析未被发现的精神和/或身体疾病；特别是那些孤独和脆弱的人，由于某种原因，他们可能无法在物理或虚拟空间中表达自己的真实感受。然而，他们的在场、缺席、参与、自我表达和与他人的口头或书面交流的模式可能有助于揭示身体或精神上的疾病，否则这些疾病可能不会被发现，而且可能确实很严重，需要紧急关注。因此，论文认为，利用机器学习和分析技术，应该能够发现用户在线角色的行为变化，如上述模式所示，这些变化可能与精神或身体健康的发作或恶化有关，如自杀念头、临床抑郁、焦虑和强迫症，或者确实与脑膜炎等严重威胁生命的传染病的发作有关。数据资产与道德首先，以道德为导向的数据资产工程方法将确保获取具有数据主体身份的开放数据源。在项目的整个生命周期中，可能会根据各种人格障碍的既定行为概况生成一些合成数据，用于建立模型和测试目的。该博士研究要求对各种人工智能和数据分析技术进行具有挑战性和创新性的部署，特别是元数据、本体和隐私工程。自然语言处理，时空数据流挖掘，不确定性建模和生成网络。本研究由EPSRC和数学、统计和计算科学学院的奖学金资助，根据EPSRC合同编号