AUTHOR=Assuied Julien , Gandica Yérali TITLE=The detection and effect of social events on Wikipedia data-set for studying human preferences JOURNAL=Frontiers in Big Data VOLUME=6 YEAR=2023 URL=https://www.frontiersin.org/journals/big-data/articles/10.3389/fdata.2023.1077318 DOI=10.3389/fdata.2023.1077318 ISSN=2624-909X ABSTRACT=

Several studies have used Wikipedia (WP) data-set to analyse worldwide human preferences by languages. However, those studies could suffer from bias related to exceptional social circumstances. Any massive event promoting exceptional editions of WP can be defined as a source of bias. In this article, we follow a procedure for detecting outliers. Our study is based on 12 languages and 13 different categories. Our methodology defines a parameter, which is language-dependent instead of being externally fixed. We also study the presence of human cyclic behavior to evaluate apparent outliers. After our analysis, we found that the outliers in our data-set do not significantly affect the analysis of preferences by categories among different WP languages. While investigating the possibility of bias related to exceptional social circumstances is always a safe measure before doing any analysis on Big Data, we found that in the case of the first ten years of the Wikipedia data-set, outliers do not significantly affect using Wikipedia data-set as a digital footprint to analyse worldwide human preferences.