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Auditing Search Query Suggestion Bias Through Recursive Algorithm Interrogation

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F22%3ASL6VQULZ" target="_blank" >RIV/00216208:11320/22:SL6VQULZ - isvavai.cz</a>

  • Result on the web

    <a href="https://doi.org/10.1145/3501247.3531567" target="_blank" >https://doi.org/10.1145/3501247.3531567</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1145/3501247.3531567" target="_blank" >10.1145/3501247.3531567</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Auditing Search Query Suggestion Bias Through Recursive Algorithm Interrogation

  • Original language description

    Despite their important role in online information search, search query suggestions have not been researched as much as most other aspects of search engines. Although reasons for this are multi-faceted, the sparseness of context and the limited data basis of up to ten suggestions per search query pose the most significant problem in identifying bias in search query suggestions. The most proven method to reduce sparseness and improve the validity of bias identification of search query suggestions so far is to consider suggestions from subsequent searches over time for the same query. This work presents a new, alternative approach to search query bias identification that includes less high-level suggestions to deepen the data basis of bias analyses. We employ recursive algorithm interrogation techniques and create suggestion trees that enable access to more subliminal search query suggestions. Based on these suggestions, we investigate topical group bias in person-related searches in the political domain.

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • OECD FORD branch

    10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Result continuities

  • Project

  • Continuities

Others

  • Publication year

    2022

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Article name in the collection

    14th ACM Web Science Conference 2022

  • ISBN

    978-1-4503-9191-7

  • ISSN

  • e-ISSN

  • Number of pages

    9

  • Pages from-to

    219-227

  • Publisher name

    Association for Computing Machinery

  • Place of publication

  • Event location

    New York, NY, USA

  • Event date

    Jan 1, 2022

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article