JMIR Publications

ABSTRACT

Background:

South Korea has the lowest fertility rate in the world, despite considerable efforts being made by the government. Increasing the fertility rate and achieving the desired outcomes of the implemented policies requires reliable data on the ongoing trends in fertility and preparations for the future based on these trends.

Objective:

This study aimed to (1) develop a determinants-of-fertility ontology with terminology for collecting and analyzing social media data, (2) determine the description logics, content coverage, and structural and representational layers of the ontology, and (3) use the ontology to detect future signals of fertility issues.

Methods:

The ontology was developed using the “Ontology Development 101” methodology. We defined the domain and scope of the ontology by compiling a list of competency questions. The terms were collected from the Korean government reports, the Basic plans for Low fertility and Aging Society, a national survey about marriage and childbirth, and social media postings on fertility issues. The classes and their hierarchy were defined using a top-down approach based on the ecological model. We defined the internal structure of classes using the entity-attribute-value model. The description logics of the ontology was evaluated using a Protégé plug-in. The content coverage was evaluated by comparing concepts extracted from social media posts with a list of classes of the ontology. The structural and representational layers of the ontology were evaluated by experts. We collected social data from 183 online channels between January 1, 2011 and June 30, 2015. To detect of future signals of fertility issues, two classes of the ontology—the socioeconomic and cultural environment, and public policy—were identified as the keywords. We constructed a keyword issue map and mapping the defined keywords to identify future signals. R software (version 3.5.2) was used for the mining of future signals.

Results:

The determinants-of-fertility ontology comprised 6 superclasses representing the component of ecological models with 230 classes, and 1,461 synonyms for classes, attributes, and values. Concept classes in the ontology were found to be coherently and consistently defined. The ontology included more than 90% of the concepts in social media posts on fertility policies. Experts gave total mean scores of 4.9 and 4.7 points for the structural layer and representational layer of the ontology, respectively. Violence and abuse (a socioeconomic and cultural factor) and flexible working arrangement (a fertility policy) were weak signals, which suggests that they could increase rapidly in the future.

Conclusions:

The determinants-of-fertility ontology developed in this study can be used as a framework for collecting and analyzing social media data on fertility issues and detecting future signals of fertility issues. The future signals identified in the study will be useful for policymakers who are developing policy responses to low fertility.