Computer Science > Computers and Society
[Submitted on 30 Oct 2025 (v1), last revised 30 Nov 2025 (this version, v3)]
Title:How Similar Are Grokipedia and Wikipedia? A Multi-Dimensional Textual and Structural Comparison
View PDF HTML (experimental)Abstract:The launch of Grokipedia - an AI-generated encyclopedia developed by Elon Musk's xAI - was presented as a response to perceived ideological and structural biases in Wikipedia, aiming to produce "truthful" entries using the Grok large language model. Yet whether an AI-driven alternative can escape the biases and limitations of human-edited platforms remains unclear. This study conducts a large-scale computational comparison of more than 17,000 matched article pairs from the 20,000 most-edited English Wikipedia pages. Using metrics spanning lexical richness, readability, reference density, structural features, and semantic similarity, we assess how closely the two platforms align in form and substance. We find that Grokipedia articles are substantially longer and contain significantly fewer references per word. Moreover, Grokipedia's content divides into two distinct groups: one that remains semantically and stylistically aligned with Wikipedia, and another that diverges sharply. Among the dissimilar articles, we observe a systematic rightward shift in the political bias of cited news sources, concentrated primarily in entries related to politics, history, and religion. These findings suggest that AI-generated encyclopedic content diverges from established editorial norms-favouring narrative expansion over citation-based verification. The implications highlight emerging tensions around transparency, provenance, and the governance of knowledge in an era of automated text generation.
Submission history
From: Taha Yasseri [view email][v1] Thu, 30 Oct 2025 18:04:46 UTC (58 KB)
[v2] Mon, 3 Nov 2025 12:50:56 UTC (247 KB)
[v3] Sun, 30 Nov 2025 22:10:18 UTC (1,986 KB)
Current browse context:
cs.CY
References & Citations
export BibTeX citation
Loading...
Bibliographic and Citation Tools
Bibliographic Explorer (What is the Explorer?)
Connected Papers (What is Connected Papers?)
Litmaps (What is Litmaps?)
scite Smart Citations (What are Smart Citations?)
Code, Data and Media Associated with this Article
alphaXiv (What is alphaXiv?)
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub (What is DagsHub?)
Gotit.pub (What is GotitPub?)
Hugging Face (What is Huggingface?)
Papers with Code (What is Papers with Code?)
ScienceCast (What is ScienceCast?)
Demos
Recommenders and Search Tools
Influence Flower (What are Influence Flowers?)
CORE Recommender (What is CORE?)
arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.