The paper linked to below (preprint) will be presented at WWW ’20 (The Web Conference) in April.
Title
Authors
Volodymyr Miz
Joëlle Hanna
Nicolas Aspert
Benjamin Ricaud
Pierre Vandergheynst
Affiliation (All Authors)
LTS2
EPFL
Lausanne, Switzerland
Source
via arXiv
Abstract
In this work, we propose an automatic evaluation and comparison of the browsing behavior of Wikipedia readers that can be applied to any language editions of Wikipedia. As an example, we focus on English, French, and Russian languages during the last four months of 2018.
The proposed method has three steps. Firstly, it extracts the most trending articles over a chosen period of time. Secondly, it performs a semi-supervised topic extraction and thirdly, it compares topics across languages. The automated processing works with the data that combines Wikipedia’s graph of hyperlinks, pageview statistics and summaries of the pages.
Source: “What is Trending on Wikipedia?…”(2020)
The results show that people share a common interest and curiosity for entertainment, e.g. movies, music, sports independently of their language. Differences appear in topics related to local events or about cultural particularities.
Interactive visualizations showing clusters of trending pages in each language edition are available online (Available April 20, 2020)
Direct to Full Text Paper
8 pages; PDF.
See Also: More Data and Resources from the Wikipedia Insights Team at EPFL.
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