Abstract
Selected web search engines provide statistics of user activities according to the topics, time and locations. The utilization requires well prepared phrases and searching range. The system of etalons for calibration searching frequencies provided by Google Trends is proposed. It was applied for evaluation of searching names of Czech towns. The regression analysis proved high correlation with population. Highlighted anomalies were explored. K-means cluster analysis enabled a categorization of selected towns. The geographical network analysis of relationships among towns suffers from low quality of locations provided by Google. The discussion includes an overview of main pros and cons of Google Trends and provides recommendations.
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Horák, J., Ivan, I., Kukuliač, P., Inspektor, T., Devečka, B., Návratová, M. (2013). Google Trends for Data Mining. Study of Czech Towns. In: Bǎdicǎ, C., Nguyen, N.T., Brezovan, M. (eds) Computational Collective Intelligence. Technologies and Applications. ICCCI 2013. Lecture Notes in Computer Science(), vol 8083. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40495-5_11
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DOI: https://doi.org/10.1007/978-3-642-40495-5_11
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