  English Wikipedia has long been an important data source for much research
and natural language machine learning modeling. The growth of non-English
language editions of Wikipedia, greater computational resources, and calls for
equity in the performance of language and multimodal models have led to the
inclusion of many more language editions of Wikipedia in datasets and models.
Building better multilingual and multimodal models requires more than just
access to expanded datasets; it also requires a better understanding of what is
in the data and how this content was generated. This paper seeks to provide
some background to help researchers think about what differences might arise
between different language editions of Wikipedia and how that might affect
their models. It details three major ways in which content differences between
language editions arise (local context, community and governance, and
technology) and recommendations for good practices when using multilingual and
multimodal data for research and modeling.
