Research and resources
This page brings together the research materials that provide the empirical foundation for the principles of the Wikimedia attribution framework. As our work progresses, we will continue to add new findings and resources to support a clearer, evidence-based understanding of the impact of attribution across reuse scenarios.
Relevant studies
Quantitative Study on Attribution: Search and AI assistants — November 2025
Research lead: Mike Raish, Lead Design Researcher at the Wikimedia Foundation
Summary of the findings
In order to begin building an empirical basis for the attribution framework, we compared 6 individual attribution signals across two contexts—Search Engine Results Page (SERP) and LLM chatbots—in a 1,186-participant, survey-based experimental study. The goal of the study was to understand the effect that individual signals might have on users' “trust” in both the information presented and Wikipedia as a source. While most signals showed similar performance across scenarios, users behaved quite differently in the two contexts: trust in LLM chat responses was noticeably shaped by the presence of attribution signals, especially when participants were more engaged with the content.
In contrast, trust in search engine results and sources was much less dependent on the presence of attribution. In this context, participants' pre-existing beliefs and browsing habits had a stronger influence than the specific signals being displayed, and differences between the signals' effects were subtle. Overall, attribution signals had a more measurable impact in the LLM context, whereas their effect in SERP environments was harder to detect.
Other relevant resources
Isaac Johnson, Nicholas Perry, Kinneret Gordon, WMF Foundation, and Jon Katz. 2021. Searching for Wikipedia: DuckDuckGo and the Wikimedia Foundation share new research on how people use search engines to get to Wikipedia. https://diff.wikimedia.org/2021/09/23/searching-for-wikipedia-duckduckgo-and-the-wikimediafoundation-share-new-research-on-how-people-use-search-engines-to-get-to-wikipedia/
Tiziano Piccardi, Miriam Redi, Giovanni Colavizza, and Robert West. 2020. Quantifying Engagement with Citations on Wikipedia. In Proceedings of The Web Conference 2020 (WWW '20). Association for Computing Machinery, New York, NY, USA, 2365–2376. https://doi.org/10.1145/3366423.3380300
Sina Semnani, Violet Yao, Heidi Zhang, and Monica Lam. 2023. WikiChat: Stopping the Hallucination of Large Language Model Chatbots by Few-Shot Grounding on Wikipedia. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 2387–2413, Singapore. Association for Computational Linguistics. https://aclanthology.org/2023.findings-emnlp.157.pdf
Nicholas Vincent and Brent Hecht. 2021. A Deeper Investigation of the Importance of Wikipedia Links to Search Engine Results. Proc. ACM Hum.-Comput. Interact. 5, CSCW1, Article 4 (April 2021), 15 pages. https://doi.org/10.1145/3449078