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%T Auditing Source Diversity Bias in Video Search Results Using Virtual Agents
%A Urman, Aleksandra
%A Makhortykh, Mykola
%A Ulloa, Roberto
%E Leskovec, Jure
%E Grobelnik, Marko
%E Najork, Marc
%E Tang, Jie
%E Zia, Leila
%P 232-236
%D 2021
%I Association for Computing Machinery
%K source diversity bias; algorithmic auditing; web search
%@ 978-1-4503-8313-4
%~ GESIS
%> https://nbn-resolving.org/urn:nbn:de:0168-ssoar-75365-2
%X We audit the presence of domain-level source diversity bias in video search results. Using a virtual agent-based approach, we compare outputs of four Western and one non-Western search engines for English and Russian queries. Our findings highlight that source diversity varies substantially depending on the language with English queries returning more diverse outputs. We also find disproportionately high presence of a single platform, YouTube, in top search outputs for all Western search engines except Google. At the same time, we observe that Youtube’s major competitors such as Vimeo or Dailymotion do not appear in the sampled Google’s video search results. This finding suggests that Google might be downgrading the results from the main competitors of Google-owned Youtube and highlights the necessity for further studies focusing on the presence of own-content bias in Google’s search results.
%C USA
%C New York
%G en
%9 Konferenzbeitrag
%W GESIS - http://www.gesis.org
%~ SSOAR - http://www.ssoar.info