Social media text mining for detecting behavioral and psychological conditions in children

用于检测儿童行为和心理状况的社交媒体文本挖掘

• 批准号: 499383-2016
• 负责人: Inkpen, Diana
• 金额: $ 10.41万
• 依托单位: University of Ottawa
• 依托单位国家: 加拿大
• 项目类别: Collaborative Research and Development Grants
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=618878
• 关键词: Social; media; text; mining; detecting

The widespread usage of social media by children has recently raised problems related to cyberbullying andother threats to their well-being. VISR.CO has an app that alerts parents to issues their kids are facing online.They analyze the social interactions kids are experiencing on social networks, and search for over 25 alertcategories, from bullying to mental health. Highly accurately detecting these issues that parents care about is adifficult Machine Learning (ML) and Natural Language Processing (NLP) challenge, requiring the latestadvancements in computer science. Additionally, when detecting mental health issues, such as anxiety,depression and self-harm, a clinical expertise is required to understand the underlying conditions and causes.They are currently able to deliver very rudimentary mental health alerts but require both NLP expertise andclinical insight.As of February 12, 2016, VISR.CO has been actively monitoring 9,985 children on behalf of 8,645 parents.VISR.CO has uncovered 850 confirmed cases of bullying (24% success rate), 452 confirmed mental healthconcerns (21% success rate), and 1,018 confirmed substance use concerns (16% success rate). We propose toresearch ways to increase our success rates by using more advanced data-mining techniques. Another challengethat they need to address is to find better ways to deal with the social media texts (such as frequentmisspellings, short-lived idiomatic expressions, and abbreviations) and to account for the dynamic nature oflinguistic expression across social media platforms.If these challenges are addressed, the product will have a competitive advantage by staying up to date,delivering useful alerts to parents, and intelligently framing the unique issues found the online activity of everychild.

最近，儿童广泛使用社交媒体引发了与网络欺凌相关的问题，并对他们的福祉构成了其他威胁。VISR.CO有一款应用程序，可以提醒父母注意他们的孩子在网上面临的问题。他们分析孩子在社交网络上经历的社交互动，并搜索超过25个警报类别，从欺凌到心理健康。高精度地检测这些家长关心的问题是一项困难的机器学习(ML)和自然语言处理(NLP)挑战，需要计算机科学的最新进步。此外，当检测到焦虑、抑郁和自我伤害等心理健康问题时，需要临床专业知识来了解潜在的状况和原因。他们目前能够发出非常基本的心理健康警报，但需要NLP专业知识和临床洞察力。截至2016年2月12日，VISR.CO代表8,645名父母积极监测9985名儿童。VISR.CO发现了850例确诊的欺凌案例(24%的成功率)，452例确诊的精神健康问题(21%的成功率)，以及1018例确诊的物质使用问题(16%的成功率)。我们建议研究如何通过使用更先进的数据挖掘技术来提高我们的成功率。他们需要解决的另一个挑战是找到更好的方法来处理社交媒体文本(如频繁的拼写错误、短暂的习语表达和缩写)，并考虑到社交媒体平台上语言表达的动态性质。如果这些挑战得到解决，该产品将通过保持最新信息、向父母发送有用的警报以及智能地框定EveryChild在线活动中发现的独特问题来获得竞争优势。