The goal of this report is to complement the literature on Wikipedia’s political bias by using a novel methodology—computational content analysis using modern LLMs for content annotation—to assess quantitatively whether there is political bias in Wikipedia’s content. Specifically, we computationally assess the sentiment and emotional tone associated with politically charged terms—those referring to politically aligned public figures and institutions—within Wikipedia articles...

In general, we find that Wikipedia articles tend to associate right-of-center public figures with somewhat more negative sentiment than left-of-center public figures; this trend can be seen in mentions of U.S. presidents, Supreme Court justices, congressmembers, state governors, leaders of Western countries, and prominent U.S.-based journalists and media organizations. We also find prevailing associations of negative emotions (e.g., anger and disgust) with right-leaning public figures and positive emotions (e.g., joy) with left-leaning public figures. In some categories of terms, such as the names of U.K. MPs and U.S.-based think tanks, we find no evidence of a difference in sentiment.

Our results suggest that Wikipedia is not living up to its stated neutral–point–of–view policy. This is concerning because we find evidence of some of Wikipedia’s prevailing sentiment associations for politically aligned public figures also popping up in OpenAI’s language models, which suggests that the political bias that we identify on the site may be percolating into widely used AI systems.