{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T16:06:23Z","timestamp":1781885183342,"version":"3.54.5"},"reference-count":12,"publisher":"Association for Computing Machinery (ACM)","issue":"1","license":[{"start":{"date-parts":[[2024,2,28]],"date-time":"2024-02-28T00:00:00Z","timestamp":1709078400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["Queue"],"published-print":{"date-parts":[[2024,2,28]]},"abstract":"<jats:p>Many people turn to Internet-based, software platforms such as Google, YouTube, Wikipedia, and more recently ChatGPT to find the answers to their questions. Most people tend to trust Google Search when it states that its mission is to deliver information from \"many angles so you can form your own understanding of the world.\" Yet, our work finds that queries involving complex topics yield results focused on a narrow set of culturally dominant views, and these views are correlated with the language used in the search phrase. We call this phenomenon language bias, and this article shows how it occurs using the example of two complex topics: Buddhism and liberalism. Language bias sets a strong yet invisible cultural barrier online with serious socio-political implications for how these platforms hinder efforts to reach across societal divides.<\/jats:p>","DOI":"10.1145\/3649303","type":"journal-article","created":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T23:33:21Z","timestamp":1710286401000},"page":"23-47","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":11,"title":["A \"Perspectival\" Mirror of the Elephant"],"prefix":"10.1145","volume":"22","author":[{"given":"Queenie","family":"Luo","sequence":"first","affiliation":[{"name":"Harvard University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael J.","family":"Puett","sequence":"additional","affiliation":[{"name":"Harvard University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael D.","family":"Smith","sequence":"additional","affiliation":[{"name":"Harvard University"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2024,3,12]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"Associated Press v. United States. (1945). 20 (Web Search p12)."},{"key":"e_1_2_1_2_1","unstructured":"Bianchi T. 2023. Global market share of leading desktop search engines 2015?2023. Statista; https:\/\/www.statista.com\/statistics\/216573\/worldwide-market-share-of-search-engines\/."},{"key":"e_1_2_1_3_1","unstructured":"Google. Our approach to search; https:\/\/www.google.com\/search\/howsearchworks\/our-approach\/."},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.findings-acl.134"},{"key":"e_1_2_1_5_1","unstructured":"Luo Q. Puett M. J. and Smith M. D. 2023. A perspectival mirror of the elephant: investigating language bias on Google ChatGPT Wikipedia and YouTube. arXiv preprint arXiv:2303.16281; https:\/\/arxiv.org\/abs\/2303.16281."},{"key":"e_1_2_1_6_1","doi-asserted-by":"crossref","unstructured":"Lutz M. Gadaginmath S. Vairavan N. and Mui P. 2021. Examining political bias within YouTube search and recommendation algorithms. IEEE Symposium Series on Computational Intelligence (SSCI); https:\/\/ieeexplore.ieee.org\/document\/9660012.","DOI":"10.1109\/SSCI50451.2021.9660012"},{"key":"e_1_2_1_7_1","volume-title":"The Web Conference; https:\/\/www.semanticscholar.org\/paper\/The-PageRank-Citation-Ranking-%3A-Bringing-Order-to-Page-Brin\/eb82d3035849cd23578096462ba419b53198a556","author":"Page L.","unstructured":"Page, L., Brin, S., Motwani, R., and Winograd, T. 1999. The PageRank citation ranking: bringing order to the web. The Web Conference; https:\/\/www.semanticscholar.org\/paper\/The-PageRank-Citation-Ranking-%3A-Bringing-Order-to-Page-Brin\/eb82d3035849cd23578096462ba419b53198a556."},{"key":"e_1_2_1_8_1","doi-asserted-by":"crossref","unstructured":"Rovira C. Codina L. and Lopezosa C. 2021. Language bias in the Google scholar ranking algorithm. Future Internet 13(2) 31; https:\/\/www.mdpi.com\/1999-5903\/13\/2\/31.","DOI":"10.3390\/fi13020031"},{"key":"e_1_2_1_9_1","volume-title":"Why liberty. Ch? Ngh?a T? do C\u00e1 Nh\u00e2n Nh? l\u00e0 m?t ch? ngh?a trung dung tri?t ??","author":"Ruper C.","unstructured":"Ruper, C. Why liberty. Ch? Ngh?a T? do C\u00e1 Nh\u00e2n Nh? l\u00e0 m?t ch? ngh?a trung dung tri?t ??. TTTD Academy; http:\/\/www.thitruongtudo.vn\/chi-tiet\/chu-nghia-tu-do-ca-nhan-nhu-la-mot-chu-nghia-trung-dung-triet-de.html."},{"key":"e_1_2_1_10_1","volume-title":"Google and the Digital Divide: The Bias of Online Knowledge","author":"Segev E.","unstructured":"Segev, E. 2010. Google and the Digital Divide: The Bias of Online Knowledge. Chandos Publishing."},{"key":"e_1_2_1_11_1","unstructured":"Wikimedia Foundation. 2023. Wikipedia: five pillars; https:\/\/en.wikipedia.org\/wiki\/Wikipedia:Five_pillars."},{"key":"e_1_2_1_12_1","volume-title":"Framing the Holocaust in popular knowledge: 3 articles about the Holocaust in English, Hebrew and Polish Wikipedia. Adeptus 8, 29?49","author":"Wolniewicz-Slomka D.","year":"2016","unstructured":"Wolniewicz-Slomka, D. 2016. Framing the Holocaust in popular knowledge: 3 articles about the Holocaust in English, Hebrew and Polish Wikipedia. Adeptus 8, 29?49; https:\/\/journals.ispan.edu.pl\/index.php\/adeptus\/article\/view\/a.2016.012."}],"container-title":["Queue"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3649303","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3649303","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T01:17:47Z","timestamp":1750295867000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3649303"}},"subtitle":["Investigating language bias on Google, ChatGPT, YouTube, and Wikipedia"],"short-title":[],"issued":{"date-parts":[[2024,2,28]]},"references-count":12,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2024,2,28]]}},"alternative-id":["10.1145\/3649303"],"URL":"https:\/\/doi.org\/10.1145\/3649303","relation":{},"ISSN":["1542-7730","1542-7749"],"issn-type":[{"value":"1542-7730","type":"print"},{"value":"1542-7749","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,2,28]]},"assertion":[{"value":"2024-03-12","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}