  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.
  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
https://wiki-insights.epfl.ch/wikitrends
