Küçük Emine Ela, Yapar Kürşad, Küçük Dilek, Küçük Doğan
Department of Public Health, Faculty of Health Sciences, Giresun University, Giresun, Turkey.
Department of Medical Pharmacology, Faculty of Medicine, Giresun University, Giresun, Turkey.
Comput Biol Med. 2017 Apr 1;83:1-9. doi: 10.1016/j.compbiomed.2017.02.001. Epub 2017 Feb 4.
Social media analysis, such as the analysis of tweets, is a promising research topic for tracking public health concerns including epidemics. In this paper, we present an ontology-based approach to automatically identify public health-related Turkish tweets. The system is based on a public health ontology that we have constructed through a semi-automated procedure. The ontology concepts are expanded through a linguistically motivated relaxation scheme as the last stage of ontology development, before being integrated into our system to increase its coverage. The ultimate lexical resource which includes the terms corresponding to the ontology concepts is used to filter the Twitter stream so that a plausible tweet subset, including mostly public-health related tweets, can be obtained. Experiments are carried out on two million genuine tweets and promising precision rates are obtained. Also implemented within the course of the current study is a Web-based interface, to track the results of this identification system, to be used by the related public health staff. Hence, the current social media analysis study has both technical and practical contributions to the significant domain of public health.
社交媒体分析，比如对推文的分析，是一个很有前景的研究课题，可用于追踪包括流行病在内的公众健康问题。在本文中，我们提出一种基于本体的方法来自动识别与土耳其公共卫生相关的推文。该系统基于我们通过半自动程序构建的公共卫生本体。在本体开发的最后阶段，通过一种基于语言学的松弛方案来扩展本体概念，然后将其集成到我们的系统中以增加其覆盖范围。包含与本体概念相对应术语的最终词汇资源用于过滤推特流，从而获得一个合理的推文子集，其中大部分是与公共卫生相关的推文。我们对两百万条真实推文进行了实验，并获得了可观的精确率。在当前研究过程中还实现了一个基于网络的界面，供相关公共卫生人员使用，以追踪这个识别系统的结果。因此，当前的社交媒体分析研究对公共卫生这一重要领域既有技术贡献，也有实际贡献。
