The Substantial Interdependence of Wikipedia and Google: A Case Study on the Relationship Between Peer Production Communities and Information Technologies

Abstract

While Wikipedia is a subject of great interest in the computing literature, very little work has considered Wikipedia’s important relationships with other information technologies like search engines. In this paper, we report the results of two deception studies whose goal was to better understand the critical relationship between Wikipedia and Google. These studies silently removed Wikipedia content from Google search results and examined the effect of doing so on participants’ interactions with both websites. Our findings demonstrate and characterize an extensive interdependence between Wikipedia and Google. Google becomes a worse search engine for many queries when it cannot surface Wikipedia content (for example, click-through rates on results pages drop significantly) and the importance of Wikipedia content is likely greater than many improvements to search algorithms. Our results also highlight Google’s critical role in providing readership to Wikipedia. However, we also found evidence that this mutually beneficial relationship is in jeopardy: changes Google has made to its search results that involve directly surfacing Wikipedia content are significantly reducing traffic to Wikipedia. Overall, our findings argue that researchers and practitioners should give deeper consideration to the interdependence between peer production communities and the information technologies that use and surface their content.

Improving Wikidata with Student-Generated Concept Maps

“…In 2014, Google committed to transferring all content from Freebase, a Google-owned knowledge graph, to Wikidata (Pellissier Tanon et al 2016), indicating their intention to continue relying on Wikidata to help populate Google Search results. However, Google does little to support Wikidata other than exploit the knowledge it contains, and may even reduce traffic to Wikidata by displaying knowledge in its own search panels without crediting Wikidata (McMahon, Johnson, and Hecht 2017).…”

Participation of New Editors after Times of Shock on Wikipedia

“…This further validates our shock identification procedure. Indeed, Wikipedia and search engines (such as Google) play an important role in each other's traffic and quality (McMahon, Johnson, and Hecht 2017).…”

TWikiL – the Twitter Wikipedia Link Dataset

“…First and foremost, however, Wikipedia is significantly shaping our digital experience. Most state-of-the-art machine learning algorithms are trained on Wikipedia content (Devlin et al 2019;Cambazoglu et al 2021), millions of searchers are dependent on Google surfacing Wikipedia links to satisfy their information needs (McMahon, Johnson, and Hecht 2017;Vincent et al 2019;Vincent and Hecht 2021), tech giants are using human workforce and Wikipedia for fact checking on their platforms (Flynn 2017;Perez 2020), Wikipedia links are among the most prominent URLs on community question answering sites like Reddit or Stack Overflow (Gómez, Cleary, and Singer 2013;Vincent, Johnson, and Hecht 2018), voice-operated virtual assistants like Siri and Alexa rely heavily on Wikipedia (Vincent and Hecht 2021) and for many users Wikipedia is a stepping stone or gateway to the larger web itself (Piccardi et al 2021). While Wikipedia is providing a lot of value to other platforms, less value is flowing back to Wikipedia in return.…”

“…Despite all this exposure, Wikipedia is not seeing an increase in page visits, a growing editor base and edits, or monetary donations. A fact that McMahon termed the paradox of reuse (McMahon, Johnson, and Hecht 2017). Recently, Wikimedia took the initiative to address this paradox and bring more balance in this value flow by launching the Wikimedia Enterprise APIs 1 , a set of API services focused on high-volume for-profit use cases like the ones mentioned above (Wikimedia 2021).…”