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Towards an understanding of the influence of social media on public healthcare by mining health information on the Social Web

通过挖掘社交网络上的健康信息来了解社交媒体对公共医疗保健的影响

基本信息

批准号：
2110166
负责人：
金额：
--
依托单位：
University of Sheffield
依托单位国家：
英国
项目类别：
Studentship
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=studentship-2110166
关键词：
Towards understanding influence social media

项目摘要

Social Media Sites are playing an increasingly important role in the generation and sharing of health information. Research has shown that a significant and increasing population is seeking and utilising health advice found on SMS1. Despite the opportunities, this creates for individual healthcare, their impact on personal health improvement is unclear. Existing research is limited and has found positive results but also that misunderstanding and misinformation are prevalent. A systematic analysis of the content of SMS is non-trivial, as such data sources are unstructured, heterogeneous, often redundant, and can be contradictory. Using the online diabetes community as a case study, we will employ Natural Language Processing techniques to automatically extract concepts, named entities, and relations to identify facts (e.g., diabetes causes dry itchy skin) mentioned from heterogeneous SMS, and integrate and link them in a structured Knowledge Base. The facts will also be quantified based on their frequency of mentions across disparate sources. The KB will capture the health information on SMS in a structured way, facilitating complex querying such as 'how many diabetes patients suffer from itchy skins and what remedies are they using'. This will enable the first crucial steps towards a quantitative and qualitative analysis of health information from SMS.

社交媒体网站在健康信息的生成和共享方面发挥着越来越重要的作用。研究表明，越来越多的人正在寻求和利用SMS1上的健康建议。尽管这为个人医疗保健创造了机会，但它们对个人健康改善的影响尚不清楚。现有的研究是有限的，已经发现了积极的结果，但误解和错误信息也很普遍。对SMS内容的系统分析是非常重要的，因为此类数据源是非结构化的、异构的、经常是冗余的，并且可能是矛盾的。以在线糖尿病社区为例，我们将采用自然语言处理技术从异构短信中自动提取概念、命名实体和关系，以识别提到的事实（例如，糖尿病导致皮肤干燥瘙痒），并将它们集成和链接到一个结构化的知识库中。这些事实也将根据它们在不同来源中被提及的频率进行量化。知识库将以一种结构化的方式获取SMS上的健康信息，方便复杂的查询，如“有多少糖尿病患者皮肤发痒，他们使用什么疗法”。这将使我们能够迈出关键的第一步，对SMS提供的健康信息进行定量和定性分析。