An approach for using wikipedia to measure the flow of trends across countries

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An approach for using wikipedia to measure the flow of trends across countries is a 2013 conference paper written in English by Tinati R., Tiropanis T., Carr L. and published in WWW 2013 Companion - Proceedings of the 22nd International Conference on World Wide Web.

[edit] Abstract

Wikipedia has grown to become the most successful online encyclopedia on the Web, containing over 24 million articles, offered in over 240 languages. In just over 10 years Wikipedia has transformed from being just an encyclopedia of knowledge, to a wealth of facts and information, from articles discussing trivia, political issues, geographies and demographics, to popular culture, news articles, and social events. In this paper we explore the use of Wikipedia for identifying the flow of information and trends across the world. We start with the hypothesis that, given that Wikipedia is a resource that is globally available in different languages across countries, access to its articles could be a reflection human activity. To explore this hypothesis we try to establish metrics on the use of Wikipedia in order to identify potential trends and to establish whether or how those trends flow from one county to another. We subsequently compare the outcome of this analysis to that of more established methods that are based on online social media or traditional media. We explore this hypothesis by applying our approach to a subset of Wikipedia articles and also a specific worldwide social phenomenon that occurred during 2012; we investigate whether access to relevant Wikipedia articles correlates to the viral success of the South Korean pop song, "Gangnam Style" and the associated artist "PSY" as evidenced by traditional and online social media. Our analysis demonstrates that Wikipedia can indeed provide a useful measure for detecting social trends and events, and in the case that we studied; it could have been possible to identify the specific trend quicker in comparison to other established trend identification services such as Google Trends.

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