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A key pitfall for knowledge-seekers, particularly in the political arena, is informed complacency, or an over-reliance on search engines at the cost of epistemic curiosity. Recent scholarship has documented significant problems with those sources of knowledge that the public relies on the most, including instances of ideological and algorithmic bias in Wikipedia and Google. Such observations raise the question of how deep one would actually need to dig into these platforms’ representations of factual (historical and biographical) knowledge before encountering similar epistemological issues. The present article addresses this question by ‘nitpicking’ knowledge representations of governments and governmental leadership in Wikipedia and Wikidata. Situated within the emerging framework of ‘data studies’, our micro-level analysis of the representations of Belgian prime ministers and their governments thereby reveals problems of classification, naming and linking of biographical items that go well beyond the affordances of the platforms under discussion. This article thus makes an evidence-based contribution to the study of the fundamental challenges that mark the formalisation of knowledge in the humanities.

Nitpicking online knowledge representations of governmental leadership. The case of Belgian prime ministers in Wikipedia and Wikidata.