How Large Language Models Source Brand Reputation Across Languages and Markets
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Computer Science > Information Retrieval
Title:How Large Language Models Source Brand Reputation Across Languages and Markets
Abstract:When a large language model (LLM) answers a question about a company, it grounds the answer in retrieved web sources, and those sources decide what the model says. Most analysis of AI brand visibility looks at the answer text. This study looks one step earlier, at the citations. We merge three this http URL datasets covering 128 brands across 12 home markets and 13 languages, and analyse 167,551 URL-grounded citations (189,974 total attribution rows). We classify each citation by domain and source type and measure where AI gets its brand information, by language and by market. Four patterns hold. First, AI grounds brand answers overwhelmingly in third-party sources: 85.7% of citations point to sites the brand does not own, against 14.3% owned. Second, the source base is concentrated and long-tailed: 80% of citations come from about 18% of domains, fitting a Zipf law (alpha = 0.86, R^2 = 0.983). Third, one reference site dominates almost everywhere: Wikipedia is the most-cited domain in 11 of 12 languages, the exception being Lithuanian, where the business daily this http URL edges it (4.38%). Fourth, the source mix is market-specific at the margin: for 46 Polish national brands the most-cited domain is YouTube, and four HR and careers portals supply 637 citations against 297 for Polish Wikipedia, about twice as many.
| Comments: | 12 pages, no figures, tables only. Data and analysis ledger on Zenodo, this https URL |
| Subjects: | Information Retrieval (cs.IR); Computation and Language (cs.CL); Computers and Society (cs.CY) |
| ACM classes: | H.3.3; H.3.5; J.4 |
| Cite as: | arXiv:2606.25787 [cs.IR] |
| (or arXiv:2606.25787v1 [cs.IR] for this version) | |
| https://doi.org/10.48550/arXiv.2606.25787
arXiv-issued DOI via DataCite (pending registration)
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