Often Hyperconnected, Seldom understood

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，对于某些人来说，归属于同一（子）群体或感兴趣的社区。社交媒体的普遍性和普遍性，作为一种“永远在线”的生活方式，同时作为一种感受在线团结的方式，也可能矛盾地成为一种感到最孤独和被误解的途径，因为参与者的各种行为，如对最脆弱者的不敏感，侵略性，欺骗性和强迫性行为。在过去的十年中，很多工作都集中在分析互联网上的行为和自我表达模式及其可能的相关性，包括精神疾病的增长趋势，特别是在青少年和年轻人中，因为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15-29岁青少年死亡的第三大原因，自杀是第二大原因”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，他们由于某种原因可能无法在物理或虚拟空间中表达他们的真实的感受。然而，他们的存在、缺席、参与、自我表达以及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疾病，否则这些疾病可能无法被发现，并且可能确实是严重的，需要紧急关注。因此，论文认为，使用机器学习和分析技术，应该可以检测用户在线角色的行为变化，如上述模式所示，其可能与精神或身体健康的发作或恶化相关，例如自杀念头、临床抑郁症、焦虑和强迫症，或者确实与严重威胁生命的传染病如脑膜炎的发作相关，败血症等。数据资产与道德首先，数据资产工程的道德指导方法将确保获取开放数据源，并且数据主体身份在源处匿名。在该项目的整个生命周期内，有可能根据各种人格障碍的既定行为概况生成一些合成数据，用于建模和测试目的。该博士研究需要各种人工智能和数据分析技术的具有挑战性和创新性的部署，特别是元数据，本体论和隐私工程。自然语言处理，时空数据流挖掘，不确定性建模和生成网络。这项研究是由EPSRC和数学，统计和计算科学学院的学生资助，根据EPSRC合同号

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