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Journal Article

Citation

Kämpf M, Tessenow E, Kenett DY, Kantelhardt JW. PLoS One 2015; 10(12): e0141892.

Affiliation

Institut für Physik, Martin-Luther-Universität Halle-Wittenberg, Sachsen-Anhalt, Germany.

Copyright

(Copyright © 2015, Public Library of Science)

DOI

10.1371/journal.pone.0141892

PMID

26720074

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.


Language: en

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