2nd BigSurv Conference (BigSurv20), 6 November - 04 December 2020
Conference Paper / Summary Text
Popular with what? Market basket analysis of Google trend topics related to female employment
Dr Anil Boz Semerci (Hacettepe University) - Presenting Author
Dr Duygu Icen (Hacettepe University)
Dr Ayse Abbasoglu Ozgoren (Hacettepe University Institute of Population Studies)
Traditional research on intentions, preferences and opinions of people on women’s employment use survey data to monitor their change in time and across countries. On the other hand, the emergence of big data provides new opportunities for monitoring and modeling attitudes towards social and economic issues. Specifically, employing trend analysis using online search data brings rewarding input to evaluate and assess changes in public opinion and perception, which can be thought as a proxy for the level of public knowledge and awareness of specific terms. This study aims to examine concepts related to female employment by using market basket analysis on Google Trends data. We aim to analyze popularity and awareness of keywords related to employment of women in a global and cross-regional setting for the period of January 2009-November 2019 using Google trends data. This study also analyzes the connectivity of keywords and their inter-associations as being consecutives or descendants using market basket analysis. We aim to reveal association rules of apriori selected keywords, which will disclose contextual information specific to regions. Regions are selected as the East and South Asia and the Europe and North America. Our data source, Google Trends, have become very popular and been widely used in assessing public opinions about several kinds of subjects. It is one of the digital data platforms that provides a time series index of the volume of queries users enter into Google in a given geographic area and provides compilations of big data. Market basket analysis is one of the used methodological approaches for working on big data. It indicates items that appear/used together and the frequency of these appearances. Such technique is appropriate in finding non-obvious and hidden associations between items, which is also crucial in assessing individuals’ thoughts on a specific topic. In this study, the association rules formed by the Apriori algorithm are listed and visualized with the help of R studio. The findings will be interpreted through the lens of gender-responsive strategies, equality, efficiency and social justice within different country and region contexts. This research contributes to the literature in several ways. First, to the best of authors’ knowledge, this study is the first one, which employs market basket analysis to Google trends data in female employment context. Second, analysing the popularity and awareness of keywords related to employment of women in a global and cross-regional setting provide comprehensive empirical ground for practical suggestions.
This project is funded by Hacettepe University Scientific Research Projects Coordination Unit, project ID SÇP-2020-18179.