The Detection of Emerging Trends Using Wikipedia Traffic Data and Context Networks

Mirko Kämpf · Eric Tessenow · Dror Y. Kenett · Jan W. Kantelhardt

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Abstract

Can online media predict new and emerging trends, since there is a relationship between trends in society and their representation in online systems? While several recent studies have used Google Trends as the leading online information source to answer corresponding research questions, we focus on the online encyclopedia Wikipedia often used for deeper topical reading. Wikipedia grants open access to all traffic data and provides lots of additional (semantic) information in a context network besides single keywords. Specifically, we suggest and study context-normalized and time-dependent measures for a topic's importance based on page-view time series of Wikipedia articles in different languages and articles related to them by internal links. As an example, we present a study of the recently emerging Big Data market with a focus on the Hadoop ecosystem, and compare the capabilities of Wikipedia versus Google in predicting its popularity and life cycles. To support further applications, we have developed an open web platform to share results of Wikipedia analytics, providing context-rich and language-independent relevance measures for emerging trends.

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2009Computational Social Science 2012Predicting the Present with Google Trends 2013Quantifying Trading Behavior in Financial Markets Using Google Trends 2012Manifesto of computational social science 2013Quantifying Wikipedia Usage Patterns Before Stock Market Moves 2013Early Prediction of Movie Box Office Success Based on Wikipedia Activity Big Data 2014Quantifying the semantics of search behavior before stock market moves 2013Quantifying the Relationship Between Financial News and the Stock Market

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