Measuring the Importance of User-Generated Content to Search Engines
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Abstract (arXiv)
Search engines are some of the most popular and profitable intelligent technologies in existence. Recent research, however, has suggested that search engines may be surprisingly dependent on user-created content like Wikipedia articles to address user information needs. In this paper, we perform a rigorous audit of the extent to which Google leverages Wikipedia and other user-generated content to respond to queries. Analyzing results for six types of important queries (e.g. most popular, trending, expensive advertising), we observe that Wikipedia appears in over 80% of results pages for some query types and is by far the most prevalent individual content source across all query types. More generally, our results provide empirical information to inform a nascent but rapidly-growing debate surrounding a highly-consequential question: Do users provide enough value to intelligent technologies that they should receive more of the economic benefits from intelligent technologies?
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 Jun 2019.
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Citing papers per year: 2021: 2; 2022: 0; 2023: 1; 2024: 1; 2025: 1.
5 papers.
- 2021Problematic Machine Behavior: A Systematic Literature Review of Algorithm Auditscited by 20
- 2025Characterizing Knowledge Manipulation in a Russian Wikipedia Forkcited by 1
- 2021Advances in Electron Microscopy with Deep Learningcited by 0
- 2023Toxic comments reduce the activity of volunteer editors on Wikipediacited by 0
- 2024Congestion Forecast for Trains with Railroad-Graph-based Semi-Supervised Learning using Sparse Passenger Reportscited by 0
- 2025Characterizing Knowledge Manipulation in a Russian Wikipedia Fork14 Apr 2025
- 2024Congestion Forecast for Trains with Railroad-Graph-based Semi-Supervised Learning using Sparse Passenger Reports23 Oct 2024
- 2023Toxic comments reduce the activity of volunteer editors on Wikipedia26 Apr 2023
- 2021Problematic Machine Behavior: A Systematic Literature Review of Algorithm Audits3 Feb 2021
- 2021Advances in Electron Microscopy with Deep Learning4 Jan 2021