I did not need a study to know this. Simply look at the Wikipedia entry for Donald Trump and it tells you everything you need to know.
David Rozado writes at City Journal:
Wikipedia’s Neutrality: Myth or Reality?Wikipedia has long been celebrated for its stated mission of providing open, unbiased information to anyone with Internet access. Central to that purpose is the site’s neutral point of view (NPOV) policy, which requires articles to be “fairly” and “proportionately” written, without “editorial bias.” My new computational analysis of Wikipedia’s content, however, found that this worthy ideal is not always realized in practice.My study examined the average sentiment—positive, negative, or neutral—associated with 1,628 politically charged terms in English Wikipedia articles. This method, which reviewed Wikipedia mentions of hundreds of politicians, journalists, and more, sought to identify whether Wikipedia biases its content based on a public figure’s political orientation.My analysis found that Wikipedia was more likely to portray right-leaning figures negatively than their left-leaning counterparts. This “sentiment bias” was apparent across groups, including United States presidents, senators, representatives, governors, Supreme Court justices, and journalists. Notably, the disparity was not universal; I did not find significant sentiment bias, for example, in the site’s descriptions of U.K. Members of Parliament.Still, these findings are concerning, particularly given the possibility of Wikipedia’s biases permeating endeavors beyond the site itself. Since the online encyclopedia’s content is used to train large language models (LLMs), which drive many cutting-edge AI systems such as ChatGPT, Wikipedia’s biases could influence AI-generated content. In fact, my analysis revealed preliminary evidence that this might already be happening: I found that the sentiment-associations in Wikipedia content and those of OpenAI’s “words embeddings layer”—a component in AI models that represents words as numerical vectors to capture their meanings and relationships—showed a slight similarity in political bias.
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