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

社交媒体网站在健康信息的产生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。对短信内容的系统分析不是微不足道的，因为这样的数据源是非结构化的、异类的、通常是冗余的，并且可能是相互矛盾的。以在线糖尿病社区为例，我们将使用自然语言处理技术自动提取概念、命名实体和关系，以识别从异类短信中提到的事实(例如，糖尿病导致皮肤干燥发痒)，并将它们整合和链接到结构化知识库中。这些事实还将根据它们在不同来源中被提及的频率进行量化。知识库将以结构化的方式捕获短信上的健康信息，便于进行复杂的查询，例如：有多少糖尿病患者皮肤发痒，他们正在使用什么治疗方法。这将使对来自短信的卫生信息进行定量和定性分析的第一个关键步骤成为可能。

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.