4 days Ago By Special to National Post
By Neil Seeman and Jeff Ballabon Wikipedia was a tech startup launched in January 2003 with a disruptive goal: to disintermediate expert-edited encyclopedias. Scholars disdained Wikipedia in its early years for its communal editing features. But the site blossomed, nonetheless.
Twenty years ago, in November 2004, Wikipedia hatched its global mobile version. It grew at a torrid clip. Today, the platform hosts over 63 million articles across 300 language editions, serving as a primary source of information for billions worldwide.
While Wikipedia’s collaborative model has democratized knowledge creation, our analysis reveals alarming patterns of bias that can cascade through the digital information ecosystem, infecting everything from search engine results to academic citations to social media posts and even AI training data. We conducted a comprehensive analysis of Wikipedia’s structural bias, using as our case study the page about South Africa’s genocide case against Israel at the International Court of Justice . Our findings unearthed patterns of systematic bias that can shape and contort public understanding of critical global issues.
Through a detailed examination of over 1,000 page revisions, we identified several key mechanisms through which bias can enter and metastasize inside Wikipedia. Our analysis identified 27 highly active editors who contributed significantly to the page. These weren’t hobbyist contributors — they averaged over 200,000 edits across Wikipedia, suggesting they’re highly experienced editors with considerable influence over content.
The bias expression analysis identified patterns of anti-Israel bias among power-user editors, highlighting how personal viewpoints can seep into supposedly neutral content. For instance, one high-bias editor consistently removed neutral descriptive terms from the Israeli response section. Another editor systematically changed article titles from neutral legal terminology (“South Africa v.
Israel (Genocide Convention)”) to more emotionally loaded versions (“South Africa’s genocide case against Israel”), demonstrating a pattern of bias in framing the conflict. Another editor imbued the page with selective emphasis in sections like “other international responses,” which skewed the narrative. Similarly, one high-bias editor invoked overt animus, such as labelling Israel as the enemy and actively accusing it of genocide, as opposed to objectively describing South Africa’s legal case.
One contributor, “EthanRossie2000,” wrote: “Free Palestine. Israel is the enemy. They’re committing genocide.
” That contribution was made when several automated bots and other editors were making category and citation changes. Notably, the comment was surrounded by seemingly routine edits from other users. It appears the comment was made without any attempt to mask its bias and there were no immediate challenges or responses to EthanRossie2000’s point of view.
And this, to be clear, was in a page ostensibly documenting the legal case in support of South Africa’s case. EthanRossie2000 was editorializing, not citing data or evidence. These findings illustrate the challenges Wikipedia faces in maintaining objectivity, particularly in articles related to geopolitics and international relations.
Neutralizing these biases requires robust editorial guidelines and oversight mechanisms to prevent the dissemination of skewed information that could mislead readers and influence public perception. This concentration of editorial power raises questions about representation and diverse perspectives in Wikipedia’s coverage of complex geopolitical issues. It also raises the spectre that the system is too readily gamed by those with a sharp axe to wield.
Given Wikipedia’s increasing reach into classrooms and hundreds of thousands of news sites, these questions demand scrutiny. A 2014 study by researchers at the University of Ottawa found 1,433 full-text articles from 1,008 journals with 2,049 Wikipedia citations. The frequency of Wikipedia citations in academic literature increased over time, with most citations occurring after December 2010.
Given the rise in scholarly citations to Wikipedia, evidence of potential agenda-driven bias renders Wikipedia less credible, let alone authoritative, for such purposes. We developed a framework for detecting bias indicators, categorizing them into “strong bias” terms (such as specific loaded terminology) and “contextual bias phrases” that shape narrative framing. This methodology revealed demonstrable patterns of selective emphasis and de-emphasis that can significantly sway readers.
Our analysis surfaced eight biases that go unmentioned in Wikipedia’s own page disclosing the traditional bias reportedly hewn in Wikipedia’s coverage of topics — specifically, how the platform “over-represents a point of view (POV) belonging to a particular demographic described as the ‘average Wikipedian,’ who is an educated, technically inclined, English-speaking white male, aged 15–49, from a developed Christian country in the northern hemisphere.” But the biases we identified in the Wikipedia page about South Africa’s genocide case against Israel at the International Court of Justice were very different. What’s particularly remarkable is these biases contradict the spirit of a “wiki” — an ethos of bottom-up collaboration and respect expressed toward all its volunteer editors.
These biases include: elite theory bias, that is, a preference for academic sources over grassroots knowledge; high-contributor frequency bias (disproportionate influence of frequent editors); citation gaming (strategic use of citations to push particular viewpoints); temporal bias (over-representation of recent events or perspectives); institutional capture systematic bias (from organized editing groups); language complexity bias (use of complex language to obscure bias); and source selectivity bias (selective choice of sources to support particular views). Despite these evident biases, contributors can always claim they are inserting a “neutral point of view” (NPOV) while expressing bias through selective editing. Bias can be readily masked through highly technical and academic language.
Perhaps most concerning is how these biases can be amplified through digital ecosystems. Whenever Wikipedia content is cited by news media, on social media, in academic papers and books, or gets used to train AI systems, these biases can be reproduced and magnified, creating self-reinforcing cycles of misinformation. But the implications extend far beyond any single Wikipedia page or topic.
Our findings suggest the need for enhanced transparency in Wikipedia’s collaborative editorial processes; the development of better tools for detecting and measuring systematic bias; greater diversity in editor demographics and viewpoints; and improved mechanisms for balancing competing narratives in controversial topics. Just as cigarette packages carry health warnings, our findings suggest the need for explicit literacy guidance on Wikipedia pages covering contentious topics. Such notices could alert readers to potential systemic biases and encourage critical engagement with the content.
Implementing a community-based fact-checking system — similar to social media platforms’ community notes but with stricter sourcing requirements — could help surface potential biases and provide balanced perspectives. These corrective annotations would require independent, verifiable sources before publication, ensuring that the additional context itself maintains high standards of accuracy and neutrality. We encourage new analyses of Wikipedia coverage across other topics such as public health, poverty, war and geopolitics.
If Wikipedia’s system is indeed being “hacked,” information on which policy-makers and the public rely turns unreliable. It can be corrupted and deployed for harmful agendas. Our goal isn’t to undermine Wikipedia — an invaluable resource — but to strengthen it by understanding its limitations and biases.
Our research demonstrates that while Wikipedia has revolutionized access to knowledge, it requires continuous scrutiny and improvement. We must ensure that the next generation of digital knowledge platforms builds on Wikipedia’s successes while addressing its systematic biases. As in social media echo chambers, biases can propagate through networks of interlinked Wikipedia articles.
When a biased perspective takes root in one article, it can spread through citations and cross-references to related pages, creating what we consider “bias clusters” that can dominate entire topics or subject areas. The future of digital knowledge depends not just on accumulating information, but on understanding how that information is shaped, shared and perpetuated through our increasingly connected information ecosystems. Special to National Post Neil Seeman is a Senior Fellow at Massey College in the University of Toronto.
Jeff Ballabon is Senior Counsel for International and Government Affairs at the American Center for Law and Justice..
